from IPython.display import display
import arcgis
from arcgis.gis import GIS
import pandas as pd
pd.set_option('display.max_columns', 500)
from arcgis.features import FeatureLayer
from arcgis.mapping import WebMap
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import time
gis = GIS("https://datascienceqa.esri.com/portal", "portaladmin", "esri.agp", verify_cert=False)
# gis = GIS("https://datascienceqa.esri.com/portal", "portaladmin", "esri.agp")
# Layers Used
# Provider data layer
# provider_layer = FeatureLayer("https://datascienceqa.esri.com/server/rest/services/Hosted/provider_clean_data_geocoded_6_19/FeatureServer/0")
provider_layer = FeatureLayer("https://datascienceqa.esri.com/server/rest/services/Hosted/provider_data_geocoded_7_23/FeatureServer/0")
# Population Density
from arcgis.mapping import MapImageLayer
popdensity = MapImageLayer("https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer")
# Median Income
medIncome = MapImageLayer("https://datascienceqa.esri.com/portal/sharing/servers/3e5f8ebe5a114a61b7f350e7a1203761/rest/services/USA_Demographics_and_Boundaries_2018/MapServer")
# Median Age
medAge = MapImageLayer("https://datascienceqa.esri.com/portal/sharing/servers/e2558ac0c5e04235ad7820773e89d110/rest/services/USA_Demographics_and_Boundaries_2018/MapServer")
# Health Insurance Coverage
tx_insurance_state = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Health_Insurance_Boundaries/FeatureServer")
tx_insurance_county = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Health_Insurance_Boundaries/FeatureServer/1")
tx_insurance_tract = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Health_Insurance_Boundaries/FeatureServer/2")
# Fertility layers for State, County and Tract (Percent of women 15 to 50 who had a birth in the past 12 months)
fertility_state = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/0")
fertility_county = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/1")
fertility_tract = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/2")
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-4-1123c4387856> in <module> 6 # Population Density 7 from arcgis.mapping import MapImageLayer ----> 8 popdensity = MapImageLayer("https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer") 9 10 # Median Income ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\mapping\_types.py in __init__(self, url, gis) 1962 super(MapImageLayer, self).__init__(url, gis) 1963 -> 1964 self._populate_layers() 1965 self._admin = None 1966 try: ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\mapping\_types.py in _populate_layers(self) 1979 tables = [] 1980 -> 1981 for lyr in self.properties.layers: 1982 if 'subLayerIds' in lyr and lyr.subLayerIds is not None: # Group Layer 1983 lyr = Layer(self.url + '/' + str(lyr.id), self._gis) ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\gis\__init__.py in properties(self) 9534 else: 9535 self._hydrate() -> 9536 return self._lazy_properties 9537 9538 @properties.setter AttributeError: 'MapImageLayer' object has no attribute '_lazy_properties'
Provider data was geocoded using the GeoAnalytics server. Let's get the geocoded provider data feature layer for exploration.
# search_result = gis.content.search('title: npidata_Geocoded')
search_result = gis.content.search('title: provider_data_geocoded_7_30', 'Feature Layer')
provider_data_item = search_result[0]
provider_data_item
provider_data_item.layers
[<FeatureLayer url:"https://datascienceqa.esri.com/server/rest/services/Hosted/provider_data_geocoded_7_30/FeatureServer/0">]
provider_data_layer = provider_data_item.layers[0]
provider_data_layer
<FeatureLayer url:"https://datascienceqa.esri.com/server/rest/services/Hosted/provider_data_geocoded_7_30/FeatureServer/0">
# Look at the fields and their data types
for f in provider_data_layer.properties.fields:
print(f['name'],' ',f['type'])
objectid esriFieldTypeOID user_npi esriFieldTypeDouble user_entity_type esriFieldTypeString user_organization_name esriFieldTypeString user_address esriFieldTypeString user_address2 esriFieldTypeString user_city esriFieldTypeString user_region esriFieldTypeString user_postal esriFieldTypeDouble user_country esriFieldTypeString user_provider_gender esriFieldTypeString user_taxonomy_code_1 esriFieldTypeString user_taxonomy_code_2 esriFieldTypeString user_taxonomy_code_3 esriFieldTypeString user_taxonomy_code_4 esriFieldTypeString user_taxonomy_code_5 esriFieldTypeString user_taxonomy_code_6 esriFieldTypeString user_taxonomy_code_7 esriFieldTypeString user_taxonomy_code_8 esriFieldTypeString user_taxonomy_code_9 esriFieldTypeString user_taxonomy_code_10 esriFieldTypeString user_taxonomy_code_11 esriFieldTypeString user_taxonomy_code_12 esriFieldTypeString user_taxonomy_code_13 esriFieldTypeString user_taxonomy_code_14 esriFieldTypeString user_taxonomy_code_15 esriFieldTypeString user_taxonomy_group_1 esriFieldTypeString user_full_address esriFieldTypeString in_address esriFieldTypeString in_address2 esriFieldTypeString in_city esriFieldTypeString in_postal esriFieldTypeDouble in_region esriFieldTypeString status esriFieldTypeString score esriFieldTypeDouble match_addr esriFieldTypeString addr_type esriFieldTypeString loc_name esriFieldTypeString longlabel esriFieldTypeString shortlabel esriFieldTypeString type esriFieldTypeString placename esriFieldTypeString place_addr esriFieldTypeString phone esriFieldTypeString url esriFieldTypeString rank esriFieldTypeDouble buildingname esriFieldTypeString addressnumber esriFieldTypeString addnumfrom esriFieldTypeString addnumto esriFieldTypeString addressrange esriFieldTypeString side esriFieldTypeString stpredir esriFieldTypeString stpretype esriFieldTypeString stname esriFieldTypeString sttype esriFieldTypeString stdir esriFieldTypeString bldgtype esriFieldTypeString bldgname esriFieldTypeString leveltype esriFieldTypeString levelname esriFieldTypeString unittype esriFieldTypeString unitname esriFieldTypeString subaddress esriFieldTypeString staddr esriFieldTypeString block esriFieldTypeString sector esriFieldTypeString neighborhood esriFieldTypeString district esriFieldTypeString city esriFieldTypeString metroarea esriFieldTypeString subregion esriFieldTypeString region esriFieldTypeString regionabbr esriFieldTypeString territory esriFieldTypeString zone esriFieldTypeString postal esriFieldTypeString postalext esriFieldTypeString country esriFieldTypeString langcode esriFieldTypeString distance esriFieldTypeDouble x esriFieldTypeDouble y esriFieldTypeDouble displayx esriFieldTypeDouble displayy esriFieldTypeDouble x_min esriFieldTypeDouble x_max esriFieldTypeDouble y_min esriFieldTypeDouble y_max esriFieldTypeDouble extrainfo esriFieldTypeString
# Check number of records in `provider_data_layer`
provider_data_layer.query(return_count_only=True)
5943659
Let's do a random test to check geocoded data. For a given state, we will look at and plot the data points based on address provided vs address geocoded.
# Create a spatially enabled dataframe for WY
%time wy_df = provider_data_layer.query(where="user_Region='WY'", as_df=True)
# %time wy_df = provider_data_layer.query(where="USER_Provider_Business_Practice_Location_Address_State_Name='WY'", as_df=True)
wy_df.shape
Wall time: 54.3 s
(12770, 91)
# Look at data points that are geocoded outside of Wyoming
len(wy_df[wy_df['region']!='Wyoming'])
3
# Check the accuracy of geocoding
round(100-((wy_df.shape[1]/wy_df.shape[0])*100),2)
99.29
map1 = gis.map('USA')
map1
# Plot data points for WY
wy_df.spatial.plot(map1)
True
map1.take_screenshot()
map1.remove_layers()
True
We have an accuracy of >99% with geocoding. From this map, we can see that out of 12770 points that have been geocoded only 3 are outside of WY. Similar analysis can be performed for other states to check for geocoding errors.
# Create a spatially enabled dataframe for AZ
# %time az_df = provider_data_layer.query(where="user_Region='AZ'", as_df=True)
%time az_df = provider_data_layer.query(where="USER_Provider_Business_Practice_Location_Address_State_Name='AZ'", as_df=True)
az_df.shape
Wall time: 19min
(108774, 368)
# Look at data points that are geocoded outside of Arizona
len(az_df[az_df['Region']!='Arizona'])
7582
# Create a spatially enabled dataframe for TX
%time tx_df = provider_data_layer.query(where="user_Region='TX'", as_df=True)
tx_df.shape
Wall time: 4min
(365783, 93)
# Look at data points that are geocoded outside of Texas
len(tx_df[tx_df['Region']!='Texas'])
17
To visualize how healthcare providers are distributed, let's do a heatmap of providers in the United States.
map_usa = gis.map('USA')
map_usa
# Add provider data and create a heatmap
usa_layer = FeatureLayer("https://datascienceqa.esri.com/server/rest/services/Hosted/provider_clean_data_geocoded_6_19/FeatureServer")
renderer = {"renderer": "autocast", #This tells python to use JS autocasting
"type": "heatmap",
# "minScale":100,
# "maxScale":10,
"blurRadius":1, # changes the size of the clusters
"maxPixelIntensity":2,
"minPixelIntensity":0,
"field":None}
renderer["colorStops"] = [{"ratio":0,"color":[63, 40, 102, 0]},
{"ratio":0.25,"color":[167,97,170,179]},
{"ratio":0.50,"color":"#7b3ce9"},
{"ratio":0.75,"color":[222,102,0,179]},
{"ratio":1,"color":[244,204,0,179]}]
# {"ratio":1,"color":"#ffff00"}]
map_usa.add_layer(usa_layer,
{ "type": "FeatureLayer",
"renderer": renderer,
})
map_usa.legend = True
map_usa.remove_layers()
True
map_usa.take_screenshot(set_as_preview=True)
The map shows distribution of providers throughout the US. We can see large gaps in Nevada, Idaho, Utah, Wyoming and Texas. These and other states can be explored further for analysis.
The second largest state in U.S. both by area and population, Texas baosts of 261,231.71 sq mi land area with a population of ~28.7 million. The population density is low with just 105.2 people per square mile and varies drastically between heavely populated urban areas to sparsely populated rural.
As seen in the previous notebook, Texas has the fourth largest number of healthcare providers in U.S. However, the state stands second highest on Health Resources and Services Administration's (HRSA) list of shortage areas. Texas also tops HRSA's list of medically underserved areas.
Let's start our journey with Texas!
# Search for Population data layer
popsearch_result = gis.content.search('title: 2018 USA Population Density')
popsearch_result
[<Item title:"2018 USA Tapestry Segmentation" type:Map Image Layer owner:esri_livingatlas>, <Item title:"2018 USA Population Density" type:Map Image Layer owner:esri_livingatlas>]
# Get Population Density
popdensity = popsearch_result[1]
popdensity
Let's bring the 2018 USA Population Density and Provider Data layers together to explore providers with respect to population density.
# Create Map
tx_pop_map = gis.map('USA')
tx_pop_map
We will use ClassedColorRenderer, a Smart Mapping feature, to automatically generate color classes for population density. The renderer is then applied to different State, City, County, Tract and Block Group layers of the population density layer.
# Add population density for Texas
for layer in popdensity.layers:
# if "43" in layer.url or "44" in layer.url:
if layer.properties['id'] in [43,44,45,46,47,48,49]:
tx_pop_map.add_layer(layer, { "type": "FeatureLayer",
"renderer":"ClassedColorRenderer",
# "definition_expression" : "STATE_NAME='Utah' or STATE_NAME='Nevada' or STATE_NAME='Idaho' or STATE_NAME='Wyoming'",
"definition_expression" : "STATE_NAME='Texas'",
"opacity":0.7,
"field_name":"POPDENS_CY"})
We will now plot healthcare providers as points on top of the population density layer.
# Add provider data layer for Texas
tx_pop_map.add_layer(provider_data_layer,
{ "type": "FeatureLayer",
"definition_expression" : "Region = 'Texas'",
"opacity":0.7})
# Add Legend
tx_pop_map.legend = True
tx_pop_map.remove_layers()
True
tx_pop_map.take_screenshot()
Let's bring the 2018 USA Median Household Income and Provider Data layers together to explore providers with respect to median income.
# Create Map
tx_income_map = gis.map('USA', 6)
tx_income_map
We will use ClassedColorRenderer, a Smart Mapping feature, to automatically generate color classes for median income. The renderer is then applied to different State, City, County, Tract and Block Group levels. We will use the median household income variable of the Census Data layer for this analysis.
# Add Median Household Income for Texas
for layer in popdensity.layers:
if layer.properties['id'] in [43,44,45,46,47,48,49]:
tx_income_map.add_layer(layer, { "type": "FeatureLayer",
"renderer":"ClassedColorRenderer",
"definition_expression" : "STATE_NAME='Texas'",
"opacity":0.7,
"field_name":"MEDHINC_CY"})
# Add provider data layer for Texas
tx_income_map.add_layer(provider_data_layer,
{ "type": "FeatureLayer",
"definition_expression" : "Region = 'Texas'",
"opacity":0.7})
# Add Legend
tx_income_map.legend = True
tx_income_map.remove_layers()
True
tx_income_map.take_screenshot()
Let's bring the 2018 USA Median Age and Provider Data layers together to explore providers with respect to median age.
# Create Map
tx_age_map = gis.map('USA', 6)
tx_age_map
We will use ClassedColorRenderer, a Smart Mapping feature, to automatically generate color classes for median income. The renderer is then applied to different State, City, County, Tract and Block Group levels. We will use the median age for current year (2018) variable of the Census Data layer for this analysis.
# Add Median Age for Texas
for layer in popdensity.layers:
# if "43" in layer.url or "44" in layer.url:
if layer.properties['id'] in [43,44,45,46,47,48,49]:
tx_age_map.add_layer(layer, { "type": "FeatureLayer",
"renderer":"ClassedColorRenderer",
"definition_expression" : "STATE_NAME='Texas'",
"opacity":0.7,
"field_name":"MEDAGE_CY"})
# Add provider data layer for Texas
tx_age_map.add_layer(provider_data_layer,
{ "type": "FeatureLayer",
"definition_expression" : "Region = 'Texas'",
"opacity":0.7})
# Add Legend
tx_age_map.legend = True
tx_age_map.remove_layers()
True
tx_age_map.take_screenshot()
Let's bring the Health Insurance Coverage Data and Provider Data layers together to explore providers with respect to percent of total population with no Health Insurance Coverage.
# Create Map
tx_insurance_map = gis.map('USA')
tx_insurance_map
This map shows a troubling picture of numerous counties with very few provders where percentage of population with no health insurance is among the highest in Texas. Some of the counties include:
We will use ClassedColorRenderer, a Smart Mapping feature, to automatically generate color classes for population with no health insurance. The renderer is then applied to different State, County and Tract layers of data.
# Add Health Insurance Coverage layers for State, County and Tract
from arcgis.mapping import MapImageLayer
tx_insurance_state = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Health_Insurance_Boundaries/FeatureServer")
tx_insurance_county = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Health_Insurance_Boundaries/FeatureServer/1")
tx_insurance_tract = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Health_Insurance_Boundaries/FeatureServer/2")
tx_insurance_map.add_layer(tx_insurance_state,
{ "type": "FeatureLayer",
"definition_expression" : "Name = 'Texas'",
"field_name":"B27010_calc_pctNoInsE",
"renderer":"ClassedColorRenderer",
"opacity":0.7})
tx_insurance_map.add_layer(tx_insurance_county,
{ "type": "FeatureLayer",
"definition_expression" : "STATE = 'Texas'",
"renderer":"ClassedColorRenderer",
"field_name":"B27010_calc_pctNoInsE",
"opacity":0.7})
tx_insurance_map.add_layer(tx_insurance_tract,
{ "type": "FeatureLayer",
"definition_expression" : "State = 'Texas'",
"renderer":"ClassedColorRenderer",
"field_name":"B27010_calc_pctNoInsE",
"opacity":0.7})
# Add provider data layer for Texas
tx_insurance_map.add_layer(provider_data_layer,
{ "type": "FeatureLayer",
"definition_expression" : "Region = 'Texas'",
"opacity":0.7})
# Add Legend
tx_insurance_map.legend = True
tx_insurance_map.remove_layers()
True
tx_insurance_map.take_screenshot()
state_layer.properties
--------------------------------------------------------------------------- gaierror Traceback (most recent call last) ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args) 1316 h.request(req.get_method(), req.selector, req.data, headers, -> 1317 encode_chunked=req.has_header('Transfer-encoding')) 1318 except OSError as err: # timeout error ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in request(self, method, url, body, headers, encode_chunked) 1228 """Send a complete request to the server.""" -> 1229 self._send_request(method, url, body, headers, encode_chunked) 1230 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in _send_request(self, method, url, body, headers, encode_chunked) 1274 body = _encode(body, 'body') -> 1275 self.endheaders(body, encode_chunked=encode_chunked) 1276 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in endheaders(self, message_body, encode_chunked) 1223 raise CannotSendHeader() -> 1224 self._send_output(message_body, encode_chunked=encode_chunked) 1225 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in _send_output(self, message_body, encode_chunked) 1015 del self._buffer[:] -> 1016 self.send(msg) 1017 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in send(self, data) 955 if self.auto_open: --> 956 self.connect() 957 else: ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in connect(self) 1383 -> 1384 super().connect() 1385 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in connect(self) 927 self.sock = self._create_connection( --> 928 (self.host,self.port), self.timeout, self.source_address) 929 self.sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\socket.py in create_connection(address, timeout, source_address) 706 err = None --> 707 for res in getaddrinfo(host, port, 0, SOCK_STREAM): 708 af, socktype, proto, canonname, sa = res ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\socket.py in getaddrinfo(host, port, family, type, proto, flags) 747 addrlist = [] --> 748 for res in _socket.getaddrinfo(host, port, family, type, proto, flags): 749 af, socktype, proto, canonname, sa = res gaierror: [Errno 11001] getaddrinfo failed During handling of the above exception, another exception occurred: URLError Traceback (most recent call last) <ipython-input-104-c9a48188431f> in <module> ----> 1 state_layer.properties ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\gis\__init__.py in properties(self) 9533 return self._lazy_properties 9534 else: -> 9535 self._hydrate() 9536 return self._lazy_properties 9537 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\gis\__init__.py in _hydrate(self) 9552 else: 9553 if isinstance(self._con, arcgis._impl._ArcGISConnection): -> 9554 self._lazy_token = self._con.generate_portal_server_token(self._url) 9555 else: 9556 self._lazy_token = self._con.token ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\connection.py in generate_portal_server_token(self, serverUrl, expiration) 533 if self.baseurl.endswith('/'): 534 resp = self.post('generateToken', postdata, --> 535 ssl=True, add_token=False) 536 else: 537 resp = self.post('/generateToken', postdata, ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\connection.py in post(self, path, postdata, files, ssl, compress, is_retry, use_ordered_dict, add_token, verify_cert, token, try_json, out_folder, file_name, force_bytes, add_headers) 1123 opener.addheaders = headers 1124 #print("***"+url) -> 1125 resp = opener.open(url, data=encoded_postdata.encode()) 1126 resp_data, is_file = self._process_response(resp, 1127 out_folder=out_folder, ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in open(self, fullurl, data, timeout) 523 req = meth(req) 524 --> 525 response = self._open(req, data) 526 527 # post-process response ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in _open(self, req, data) 541 protocol = req.type 542 result = self._call_chain(self.handle_open, protocol, protocol + --> 543 '_open', req) 544 if result: 545 return result ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in _call_chain(self, chain, kind, meth_name, *args) 501 for handler in handlers: 502 func = getattr(handler, meth_name) --> 503 result = func(*args) 504 if result is not None: 505 return result ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in https_open(self, req) 1358 def https_open(self, req): 1359 return self.do_open(http.client.HTTPSConnection, req, -> 1360 context=self._context, check_hostname=self._check_hostname) 1361 1362 https_request = AbstractHTTPHandler.do_request_ ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args) 1317 encode_chunked=req.has_header('Transfer-encoding')) 1318 except OSError as err: # timeout error -> 1319 raise URLError(err) 1320 r = h.getresponse() 1321 except: URLError: <urlopen error [Errno 11001] getaddrinfo failed>
from arcgis.geoanalytics import summarize_data
provider_state_agg = summarize_data.aggregate_points(point_layer = provider_data_layer,
polygon_layer = state_layer,
output_name='provider_state_agg',
summary_fields=[{"statisticType": "Count", "onStatisticField": "user_npi"}])
--------------------------------------------------------------------------- gaierror Traceback (most recent call last) ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args) 1316 h.request(req.get_method(), req.selector, req.data, headers, -> 1317 encode_chunked=req.has_header('Transfer-encoding')) 1318 except OSError as err: # timeout error ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in request(self, method, url, body, headers, encode_chunked) 1228 """Send a complete request to the server.""" -> 1229 self._send_request(method, url, body, headers, encode_chunked) 1230 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in _send_request(self, method, url, body, headers, encode_chunked) 1274 body = _encode(body, 'body') -> 1275 self.endheaders(body, encode_chunked=encode_chunked) 1276 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in endheaders(self, message_body, encode_chunked) 1223 raise CannotSendHeader() -> 1224 self._send_output(message_body, encode_chunked=encode_chunked) 1225 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in _send_output(self, message_body, encode_chunked) 1015 del self._buffer[:] -> 1016 self.send(msg) 1017 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in send(self, data) 955 if self.auto_open: --> 956 self.connect() 957 else: ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in connect(self) 1383 -> 1384 super().connect() 1385 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in connect(self) 927 self.sock = self._create_connection( --> 928 (self.host,self.port), self.timeout, self.source_address) 929 self.sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\socket.py in create_connection(address, timeout, source_address) 706 err = None --> 707 for res in getaddrinfo(host, port, 0, SOCK_STREAM): 708 af, socktype, proto, canonname, sa = res ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\socket.py in getaddrinfo(host, port, family, type, proto, flags) 747 addrlist = [] --> 748 for res in _socket.getaddrinfo(host, port, family, type, proto, flags): 749 af, socktype, proto, canonname, sa = res gaierror: [Errno 11001] getaddrinfo failed During handling of the above exception, another exception occurred: URLError Traceback (most recent call last) <ipython-input-101-b45a3f4a1ef5> in <module> 3 polygon_layer = state_layer, 4 output_name='provider_state_agg', ----> 5 summary_fields=[{"statisticType": "Count", "onStatisticField": "user_npi"}]) ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\geoanalytics\summarize_data.py in aggregate_points(point_layer, bin_type, bin_size, bin_size_unit, polygon_layer, time_step_interval, time_step_interval_unit, time_step_repeat_interval, time_step_repeat_interval_unit, time_step_reference, summary_fields, output_name, gis) 283 output_service_name = output_name.replace(' ', '_') 284 --> 285 output_service = _create_output_service(gis, output_name, output_service_name, 'Aggregate Points') 286 287 params['output_name'] = _json.dumps({ ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\geoanalytics\_util.py in _create_output_service(gis, output_name, output_service_name, task) 80 81 def _create_output_service(gis, output_name, output_service_name='Analysis feature service', task='GeoAnalytics'): ---> 82 ok = gis.content.is_service_name_available(output_name, 'Feature Service') 83 if not ok: 84 raise RuntimeError("A Feature Service by this name already exists: " + output_name) ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\gis\__init__.py in is_service_name_available(self, service_name, service_type) 4111 } 4112 -> 4113 res = self._portal.con.post(path, postdata) 4114 return res['available'] 4115 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\connection.py in post(self, path, postdata, files, ssl, compress, is_retry, use_ordered_dict, add_token, verify_cert, token, try_json, out_folder, file_name, force_bytes, add_headers) 1123 opener.addheaders = headers 1124 #print("***"+url) -> 1125 resp = opener.open(url, data=encoded_postdata.encode()) 1126 resp_data, is_file = self._process_response(resp, 1127 out_folder=out_folder, ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in open(self, fullurl, data, timeout) 523 req = meth(req) 524 --> 525 response = self._open(req, data) 526 527 # post-process response ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in _open(self, req, data) 541 protocol = req.type 542 result = self._call_chain(self.handle_open, protocol, protocol + --> 543 '_open', req) 544 if result: 545 return result ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in _call_chain(self, chain, kind, meth_name, *args) 501 for handler in handlers: 502 func = getattr(handler, meth_name) --> 503 result = func(*args) 504 if result is not None: 505 return result ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in https_open(self, req) 1358 def https_open(self, req): 1359 return self.do_open(http.client.HTTPSConnection, req, -> 1360 context=self._context, check_hostname=self._check_hostname) 1361 1362 https_request = AbstractHTTPHandler.do_request_ ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args) 1317 encode_chunked=req.has_header('Transfer-encoding')) 1318 except OSError as err: # timeout error -> 1319 raise URLError(err) 1320 r = h.getresponse() 1321 except: URLError: <urlopen error [Errno 11001] getaddrinfo failed>
Image source: https://cuindependent.com/2019/03/08/opinion-mental-health-education/
Let's explore the distribution of mental health providers in US using a heatmap.
mental_map = gis.map('USA')
mental_map
This map paints a grim picture of the availability of mental healthcare providers. There are states in every region with vast areas of NO mental health providers or very few providers. To list, some of these states include:
renderer = {"renderer": "autocast", #This tells python to use JS autocasting
"type": "heatmap",
"blurRadius":1, # changes the size of the clusters
"maxPixelIntensity":2,
"minPixelIntensity":0,
"field":None}
renderer["colorStops"] = [{"ratio":0,"color":[63, 40, 102, 0]},
{"ratio":0.25,"color":[167,97,170,179]},
{"ratio":0.50,"color":"#7b3ce9"},
{"ratio":0.75,"color":[222,102,0,179]},
{"ratio":1,"color":[244,204,0,179]}]
mental_map.add_layer(provider_data_layer,
{ "type": "FeatureLayer",
"renderer": renderer,
"definition_expression" : "user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X']"
})
mental_map.remove_layers()
True
mental_map.take_screenshot()
Let's get census data from USA_Demographics_and_Boundaries_2018 layer. We will use 'Pop per Square Mile' as the variable to create our basemap and add providers as points on top.
# Search for Population data layer
popsearch_result = gis.content.search('title: 2018 USA Population Density')
popsearch_result
[<Item title:"2018 USA Tapestry Segmentation" type:Map Image Layer owner:esri_livingatlas>, <Item title:"2018 USA Population Density" type:Map Image Layer owner:esri_livingatlas>]
# Get Population Density
popdensity = popsearch_result[1]
popdensity
mental_map2 = gis.map('USA')
mental_map2
From this map, we can see that percent of women (15 to 50) who had a birth in the past 12 months is high in Idaho, Utah, North Dakota, South Dakota and Nebraska.
# Add population density layer
for layer in popdensity.layers:
# if "43" in layer.url or "44" in layer.url:
if layer.properties['id'] in [43,44,45,46,47,48,49]:
mental_map2.add_layer(layer, { "type": "FeatureLayer",
"renderer":"ClassedColorRenderer",
# # "definition_expression" : "STATE_NAME='Utah' or STATE_NAME='Nevada' or STATE_NAME='Idaho' or STATE_NAME='Wyoming'",
# "definition_expression" : "STATE_NAME='Texas'",
"opacity":0.7,
"field_name":"POPDENS_CY"})
# Add Mental Healthcare Provider Data
mental_map2.add_layer(provider_data_layer,
{"type":"FeatureLayer",
"definition_expression":"user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X']",
"opacity":0.7})
mental_map2.remove_layers()
True
mental_map2.legend = True
mental_map2.take_screenshot()
popdensity.layers
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Let's find out the ratio of population to mental healthcare providers to understand which states have the least number of providers.
We will use the fertility layer at state level to create this dataframe
# State population dataframe
state_layer = popdensity.layers[43]
state_featureset = state_layer.query(out_fields='STATE_NAME,ST_ABBREV,TOTPOP_CY')
state_df = state_featureset.sdf
state_df.head()
| OBJECTID | SHAPE | STATE_NAME | ST_ABBREV | TOTPOP_CY | |
|---|---|---|---|---|---|
| 0 | 1 | {"rings": [[[-9747504.6398, 3539549.5786999986... | Alabama | AL | 4968383 |
| 1 | 2 | {"rings": [[[-19677908.5389, 6763775.151000001... | Alaska | AK | 750876 |
| 2 | 3 | {"rings": [[[-12138852.7978, 4438964.613399997... | Arizona | AZ | 7132147 |
| 3 | 4 | {"rings": [[[-9989041.8861, 4300705.307499997]... | Arkansas | AR | 3067536 |
| 4 | 5 | {"rings": [[[-13038833.5744, 3845968.519900001... | California | CA | 39806791 |
We will use provider_data_layer to subset mental healthcare providers
# Get provider data for mental healthcare providers only
mental_featureset = provider_data_layer.query(where="user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X']",
out_fields='user_npi,user_entity_type,user_provider_gender,user_taxonomy_code_1,user_full_address,Postal,City,Subregion,Region,RegionAbbr')
mental_df = mental_featureset.sdf
mental_df.head()
| City | OBJECTID | Postal | Region | RegionAbbr | SHAPE | Subregion | user_entity_type | user_full_address | user_npi | user_provider_gender | user_taxonomy_code_1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Kailua | 50 | 96734 | Hawaii | HI | {"x": -157.75842118599996, "y": 21.38069594300... | City and County of Honolulu | Individual | 642 ULUKAHIKI ST, SUITE 300, KAILUA, HI 967344400 | 1366445306 | M | 2084N0400X |
| 1 | Rialto | 110 | 92376 | California | CA | {"x": -117.35748267199995, "y": 34.09373249300... | San Bernardino County | Individual | 648 E GLEN OAK ST, , RIALTO, CA 923766648 | 1760500664 | F | 103T00000X |
| 2 | Colorado Springs | 113 | 80903 | Colorado | CO | {"x": -104.81990027499995, "y": 38.84235050400... | El Paso County | Individual | 224 E WILLAMETTE AVE, , COLORADO SPRINGS, CO 8... | 1023011046 | M | 103T00000X |
| 3 | Miami | 118 | 33134 | Florida | FL | {"x": -80.26657097299994, "y": 25.771643515000... | Miami-Dade County | Organization | 4343 W FLAGLER ST STE 100, , CORAL GABLES, FL ... | 1942507769 | None | 2084P0800X |
| 4 | Tamuning | 122 | 96913 | Guam | GU | {"x": 144.7754183520001, "y": 13.5051031310000... | Individual | 790 GOV. CARLOS G. CAMACHO RD., , TAMUNING, GU... | 1508862269 | M | 2084P0800X |
# Create dataframe of mental healthcare provider counts by state
mental_count_df = pd.DataFrame(mental_df['RegionAbbr'].value_counts().reset_index().values, columns=['RegionAbbr','Provider_Count'])
# Plot mental healthcare Providers by State
plt.figure(figsize=(25,12))
sns.barplot(mental_count_df['RegionAbbr'].iloc[:-19], mental_count_df['Provider_Count'].iloc[:-19])
plt.title('Mental Healthcare Providers by State', fontsize=22)
plt.xlabel('States', fontsize=18)
plt.ylabel('Provider Count', fontsize=18)
Text(0, 0.5, 'Provider Count')
# Merge provider count and women_df at state level
state_mental_df = pd.merge(mental_count_df,state_df,left_on='RegionAbbr', right_on='ST_ABBREV',how='inner')
state_mental_df.head()
| RegionAbbr | Provider_Count | OBJECTID | SHAPE | STATE_NAME | ST_ABBREV | TOTPOP_CY | |
|---|---|---|---|---|---|---|---|
| 0 | CA | 51820 | 5 | {'rings': [[[-13038833.5744, 3845968.519900001... | California | CA | 39806791 |
| 1 | NY | 23808 | 33 | {'rings': [[[-8259979.4246, 4949745.592100002]... | New York | NY | 20070143 |
| 2 | FL | 22890 | 10 | {'rings': [[[-9124037.3451, 2817150.9723999985... | Florida | FL | 20875686 |
| 3 | MI | 18107 | 23 | {'rings': [[[-9291127.6781, 5121002.034400001]... | Michigan | MI | 10057191 |
| 4 | TX | 13623 | 44 | {'rings': [[[-10822386.4159, 2999262.775600001... | Texas | TX | 28954616 |
# Create new columns that shows people per provider
state_mental_df['people_per_prov'] = state_mental_df['TOTPOP_CY']/state_mental_df['Provider_Count']
# Arrange dataframe by mother_per_prov descending
state_mental_df = state_mental_df.sort_values(by=['people_per_prov'], ascending=False)
state_mental_df.head()
| RegionAbbr | Provider_Count | OBJECTID | SHAPE | STATE_NAME | ST_ABBREV | TOTPOP_CY | people_per_prov | |
|---|---|---|---|---|---|---|---|---|
| 43 | MS | 1108 | 25 | {'rings': [[[-9952997.0803, 3545020.2216000035... | Mississippi | MS | 3051594 | 2754.15 |
| 33 | AL | 2030 | 1 | {'rings': [[[-9747504.6398, 3539549.5786999986... | Alabama | AL | 4968383 | 2447.48 |
| 37 | IA | 1396 | 16 | {'rings': [[[-10154309.0285, 5388473.678900003... | Iowa | IA | 3219046 | 2305.91 |
| 45 | ID | 790 | 13 | {'rings': [[[-12362299.9589, 5539778.200800002... | Idaho | ID | 1760131 | 2228.01 |
| 4 | TX | 13623 | 44 | {'rings': [[[-10822386.4159, 2999262.775600001... | Texas | TX | 28954616 | 2125.42 |
# Plot No. of Mothers (age 15 to 50) per OB-GYN Provider by State
plt.figure(figsize=(25,12))
sns.barplot(state_mental_df['RegionAbbr'], state_mental_df['people_per_prov'])
plt.title('No. of People per Mental Heathcare Provider by State', fontsize=22)
plt.xlabel('States', fontsize=18)
plt.ylabel('No. of People', fontsize=18)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
(array([ 0., 500., 1000., 1500., 2000., 2500., 3000.]), <a list of 7 Text yticklabel objects>)
On average, there were ~2754 people per mental healthcare provider in Mississippi compared to ~364 people per provider in DC. The difference is drastic.
Mississippi has the highest number of people per mental healthcare provider. Let's explore Mississippi to find out which counties have the lowest number of providers.
# County population df
county_layer = popdensity.layers[46]
MS_featureset = county_layer.query(where="ST_ABBREV='MS'", out_fields='ST_ABBREV,NAME,TOTPOP_CY')
MS_pop_df = MS_featureset.sdf
MS_pop_df.head()
| NAME | OBJECTID | SHAPE | ST_ABBREV | TOTPOP_CY | |
|---|---|---|---|---|---|
| 0 | Adams County | 1402 | {"rings": [[[-10195353.0856, 3667011.907600000... | MS | 31455 |
| 1 | Alcorn County | 1403 | {"rings": [[[-9838473.3692, 4163284.5766000003... | MS | 37398 |
| 2 | Amite County | 1404 | {"rings": [[[-10139507.6094, 3675190.789200000... | MS | 12573 |
| 3 | Attala County | 1405 | {"rings": [[[-9957952.5787, 3933319.5100999996... | MS | 19195 |
| 4 | Benton County | 1406 | {"rings": [[[-9917285.3423, 4109418.0692000017... | MS | 8621 |
# Get provider data for obgyn providers only
mental_MS_df = mental_df[mental_df['RegionAbbr']=='MS']
mental_MS_df.head()
mental_MS_df.shape
(1108, 12)
# Create dataframe of obgyn provider counts by county
mental_MScounty_df = pd.DataFrame(mental_MS_df['Subregion'].value_counts().reset_index().values, columns=['County','Provider_Count'])
mental_MScounty_df.head()
| County | Provider_Count | |
|---|---|---|
| 0 | Hinds County | 274 |
| 1 | Harrison County | 143 |
| 2 | Rankin County | 81 |
| 3 | Lamar County | 76 |
| 4 | Lauderdale County | 52 |
mental_MScounty_df
| County | Provider_Count | |
|---|---|---|
| 0 | Hinds County | 274 |
| 1 | Harrison County | 143 |
| 2 | Rankin County | 81 |
| 3 | Lamar County | 76 |
| 4 | Lauderdale County | 52 |
| 5 | Forrest County | 51 |
| 6 | Madison County | 48 |
| 7 | Lee County | 45 |
| 8 | Jackson County | 43 |
| 9 | Lafayette County | 40 |
| 10 | DeSoto County | 33 |
| 11 | Alcorn County | 17 |
| 12 | Warren County | 15 |
| 13 | Lowndes County | 13 |
| 14 | Pike County | 13 |
| 15 | Washington County | 12 |
| 16 | Panola County | 11 |
| 17 | Leflore County | 10 |
| 18 | Jones County | 8 |
| 19 | Lincoln County | 8 |
| 20 | Adams County | 7 |
| 21 | Pearl River County | 7 |
| 22 | Bolivar County | 6 |
| 23 | Grenada County | 6 |
| 24 | Clay County | 5 |
| 25 | Simpson County | 5 |
| 26 | Winston County | 5 |
| 27 | Oktibbeha County | 5 |
| 28 | Monroe County | 4 |
| 29 | Tippah County | 4 |
| ... | ... | ... |
| 34 | Choctaw County | 3 |
| 35 | Lawrence County | 2 |
| 36 | Newton County | 2 |
| 37 | Copiah County | 2 |
| 38 | Humphreys County | 2 |
| 39 | Quitman County | 2 |
| 40 | Sunflower County | 2 |
| 41 | Jefferson County | 2 |
| 42 | George County | 2 |
| 43 | Jefferson Davis County | 2 |
| 44 | Tate County | 2 |
| 45 | Franklin County | 2 |
| 46 | Prentiss County | 2 |
| 47 | Yazoo County | 2 |
| 48 | Pontotoc County | 1 |
| 49 | Union County | 1 |
| 50 | Marshall County | 1 |
| 51 | Scott County | 1 |
| 52 | Attala County | 1 |
| 53 | Covington County | 1 |
| 54 | Perry County | 1 |
| 55 | Jasper County | 1 |
| 56 | Clarke County | 1 |
| 57 | Benton County | 1 |
| 58 | Holmes County | 1 |
| 59 | Amite County | 1 |
| 60 | Smith County | 1 |
| 61 | Stone County | 1 |
| 62 | Neshoba County | 1 |
| 63 | Chickasaw County | 1 |
64 rows × 2 columns
# Merge provider count and women data at county level for ND
county_mental_df = pd.merge(MS_pop_df,mental_MScounty_df,left_on='NAME', right_on='County',how='left')
county_mental_df.head()
| NAME | OBJECTID | SHAPE | ST_ABBREV | TOTPOP_CY | County | Provider_Count | |
|---|---|---|---|---|---|---|---|
| 0 | Adams County | 1402 | {'rings': [[[-10195353.0856, 3667011.907600000... | MS | 31455 | Adams County | 7 |
| 1 | Alcorn County | 1403 | {'rings': [[[-9838473.3692, 4163284.5766000003... | MS | 37398 | Alcorn County | 17 |
| 2 | Amite County | 1404 | {'rings': [[[-10139507.6094, 3675190.789200000... | MS | 12573 | Amite County | 1 |
| 3 | Attala County | 1405 | {'rings': [[[-9957952.5787, 3933319.5100999996... | MS | 19195 | Attala County | 1 |
| 4 | Benton County | 1406 | {'rings': [[[-9917285.3423, 4109418.0692000017... | MS | 8621 | Benton County | 1 |
# Create new columns that shows Mental Healthcare provider by population
county_mental_df['people_per_prov'] = county_mental_df['TOTPOP_CY']/county_mental_df['Provider_Count']
# Arrange dataframe by mother_per_prov descending and then B13016_002E (women who gave birht) descending
county_mental_df = county_mental_df.sort_values(by=['people_per_prov'], ascending=False)[['NAME','TOTPOP_CY','Provider_Count','people_per_prov']]
# county_obgyn_df.columns = ['Name','Provider_Count', "Total Women (15 to 50)","Women who had birth (past 12 months)", 'women_per_prov', 'mother_per_prov']
county_mental_df
| NAME | TOTPOP_CY | Provider_Count | people_per_prov | |
|---|---|---|---|---|
| 46 | Marshall County | 37137 | 1 | 37137 |
| 57 | Pontotoc County | 32007 | 1 | 32007 |
| 49 | Neshoba County | 29483 | 1 | 29483 |
| 61 | Scott County | 29144 | 1 | 29144 |
| 72 | Union County | 28734 | 1 | 28734 |
| 15 | Covington County | 20192 | 1 | 20192 |
| 25 | Holmes County | 19589 | 1 | 19589 |
| 3 | Attala County | 19195 | 1 | 19195 |
| 65 | Stone County | 18248 | 1 | 18248 |
| 8 | Chickasaw County | 17554 | 1 | 17554 |
| 64 | Smith County | 17382 | 1 | 17382 |
| 30 | Jasper County | 17235 | 1 | 17235 |
| 11 | Clarke County | 16309 | 1 | 16309 |
| 14 | Copiah County | 29217 | 2 | 14608.5 |
| 68 | Tate County | 29215 | 2 | 14607.5 |
| 66 | Sunflower County | 28285 | 2 | 14142.5 |
| 81 | Yazoo County | 27697 | 2 | 13848.5 |
| 58 | Prentiss County | 25416 | 2 | 12708 |
| 2 | Amite County | 12573 | 1 | 12573 |
| 55 | Perry County | 12423 | 1 | 12423 |
| 22 | Hancock County | 49209 | 4 | 12302.2 |
| 19 | George County | 23952 | 2 | 11976 |
| 50 | Newton County | 21801 | 2 | 10900.5 |
| 52 | Oktibbeha County | 50893 | 5 | 10178.6 |
| 47 | Monroe County | 36744 | 4 | 9186 |
| 4 | Benton County | 8621 | 1 | 8621 |
| 33 | Jones County | 68332 | 8 | 8541.5 |
| 54 | Pearl River County | 56971 | 7 | 8138.71 |
| 38 | Lawrence County | 13464 | 2 | 6732 |
| 45 | Marion County | 25925 | 4 | 6481.25 |
| ... | ... | ... | ... | ... |
| 9 | Choctaw County | 8385 | 3 | 2795 |
| 10 | Claiborne County | 9861 | 4 | 2465.25 |
| 44 | Madison County | 110172 | 48 | 2295.25 |
| 1 | Alcorn County | 37398 | 17 | 2199.88 |
| 40 | Lee County | 86039 | 45 | 1911.98 |
| 60 | Rankin County | 152523 | 81 | 1883 |
| 37 | Lauderdale County | 78920 | 52 | 1517.69 |
| 17 | Forrest County | 75838 | 51 | 1487.02 |
| 23 | Harrison County | 206287 | 143 | 1442.57 |
| 35 | Lafayette County | 56405 | 40 | 1410.12 |
| 24 | Hinds County | 241686 | 274 | 882.066 |
| 36 | Lamar County | 59391 | 76 | 781.461 |
| 6 | Calhoun County | 14741 | NaN | NaN |
| 7 | Carroll County | 10761 | NaN | NaN |
| 20 | Greene County | 13703 | NaN | NaN |
| 27 | Issaquena County | 1403 | NaN | NaN |
| 28 | Itawamba County | 23998 | NaN | NaN |
| 34 | Kemper County | 11049 | NaN | NaN |
| 39 | Leake County | 22284 | NaN | NaN |
| 48 | Montgomery County | 10326 | NaN | NaN |
| 51 | Noxubee County | 11253 | NaN | NaN |
| 62 | Sharkey County | 4733 | NaN | NaN |
| 67 | Tallahatchie County | 15196 | NaN | NaN |
| 70 | Tishomingo County | 19971 | NaN | NaN |
| 71 | Tunica County | 10696 | NaN | NaN |
| 73 | Walthall County | 14910 | NaN | NaN |
| 76 | Wayne County | 20937 | NaN | NaN |
| 77 | Webster County | 10001 | NaN | NaN |
| 78 | Wilkinson County | 9745 | NaN | NaN |
| 80 | Yalobusha County | 12813 | NaN | NaN |
82 rows × 4 columns
Mental Health - MI
mental_map_mi = gis.map('USA')
mental_map_mi
# Add population density for Michigan
for layer in popdensity.layers:
# if "43" in layer.url or "44" in layer.url:
if layer.properties['id'] in [43,44,45,46,47,48,49]:
mental_map_mi.add_layer(layer, { "type": "FeatureLayer",
"renderer":"ClassedColorRenderer",
# "definition_expression" : "STATE_NAME='Utah' or STATE_NAME='Nevada' or STATE_NAME='Idaho' or STATE_NAME='Wyoming'",
"definition_expression" : "STATE_NAME='Michigan'",
"opacity":0.7,
"field_name":"POPDENS_CY"})
# provider_layer = FeatureLayer("https://datascienceqa.esri.com/server/rest/services/Hosted/provider_clean_data_geocoded_6_19/FeatureServer")
mental_map_mi.add_layer(provider_data_layer,
{"type":"FeatureLayer",
"definition_expression":"Region='Michigan' and (user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X'])",
# "definition_expression":"Region='Michigan'",
"opacity":0.7})
mental_map_mi.legend = True
mental_map_mi.remove_layers()
True
mental_map_mi.take_screenshot()
Mental Map - 4 states
mid_states_map = gis.map('USA')
mid_states_map
# Add population density for mid states
for layer in popdensity.layers:
# if "43" in layer.url or "44" in layer.url:
if layer.properties['id'] in [43,44,45,46,47,48,49]:
mid_states_map.add_layer(layer, { "type": "FeatureLayer",
"renderer":"ClassedColorRenderer",
"definition_expression" : "STATE_NAME='Utah' or STATE_NAME='Nevada' or STATE_NAME='Idaho' or STATE_NAME='Wyoming' or STATE_NAME='Montana' or STATE_NAME='Colorado'",
# "definition_expression" : "STATE_NAME='Michigan'",
"opacity":0.7,
"field_name":"POPDENS_CY"})
mid_states_map.add_layer(provider_data_layer,
{"type":"FeatureLayer",
"definition_expression":"(Region in ['Utah','Wyoming','Nevada','Idaho','Montana','Colorado']) and (user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X'])",
# "definition_expression":"Region='Michigan'",
"opacity":0.7})
mid_states_map.legend = True
mid_states_map.remove_layers()
True
mid_states_map.take_screenshot()
south_states_map = gis.map('USA')
# south_states_map.basemap = 'national-geographic'
south_states_map
# Add population density for mid states
for layer in popdensity.layers:
# if "43" in layer.url or "44" in layer.url:
if layer.properties['id'] in [43,44,45,46,47,48,49]:
south_states_map.add_layer(layer, { "type": "FeatureLayer",
"renderer":"ClassedColorRenderer",
"definition_expression" : "STATE_NAME='Arkansas' or STATE_NAME='Mississippi' or STATE_NAME='Louisiana' or STATE_NAME='Tennessee' or STATE_NAME='North Carolina' or STATE_NAME='South Carolina' or STATE_NAME='Alabama' or STATE_NAME='Georgia' or STATE_NAME='Florida'",
# "definition_expression" : "STATE_NAME='Michigan'",
"opacity":0.5,
"field_name":"POPDENS_CY"})
south_states_map.add_layer(provider_data_layer,
{"type":"FeatureLayer",
"definition_expression":"(Region in ['Arkansas','Mississippi','Louisiana','Tennessee','North Carolina','South Carolina','Alabama','Georgia','Florida']) and (user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X'])",
# "definition_expression":"Region='Michigan'",
"opacity":0.7})
south_states_map.legend = True
south_states_map.remove_layers()
True
south_states_map.take_screenshot()
top_states_map = gis.map('USA')
# south_states_map.basemap = 'national-geographic'
top_states_map
# Add population density for mid states
for layer in popdensity.layers:
# if "43" in layer.url or "44" in layer.url:
if layer.properties['id'] in [43,44,45,46,47,48,49]:
top_states_map.add_layer(layer, { "type": "FeatureLayer",
"renderer":"ClassedColorRenderer",
"definition_expression" : "STATE_NAME='Minnesota' or STATE_NAME='Iowa' or STATE_NAME='North Dakota' or STATE_NAME='South Dakota'",
# "definition_expression" : "STATE_NAME='Michigan'",
"opacity":0.5,
"field_name":"POPDENS_CY"})
top_states_map.add_layer(provider_data_layer,
{"type":"FeatureLayer",
"definition_expression":"(Region in ['Minnesota','Iowa','North Dakota','South Dakota']) and (user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X'])",
# "definition_expression":"Region='Michigan'",
"opacity":0.7})
top_states_map.legend = True
top_states_map.remove_layers()
True
top_states_map.take_screenshot()
Let's explore the distribution of OBGYN health providers in US using a heatmap.
Note - Provider Taxonomy codes were filtered for OB-GYN providers using this reference.
women_map = gis.map('USA')
women_map
This map paints a grim picture of the availability of OBGYN healthcare providers. We can see vast areas in Midwest and West with NO or very few OBGYN providers.
# Define renderer and add provider data for OBGYN providers
renderer = {"renderer": "autocast", #This tells python to use JS autocasting
"type": "heatmap",
"blurRadius":1, # changes the size of the clusters
"maxPixelIntensity":2,
"minPixelIntensity":0,
"field":None}
renderer["colorStops"] = [{"ratio":0,"color":[63, 40, 102, 0]},
{"ratio":0.25,"color":[167,97,170,179]},
{"ratio":0.50,"color":"#7b3ce9"},
{"ratio":0.75,"color":[222,102,0,179]},
{"ratio":1,"color":[244,204,0,179]}]
women_map.add_layer(provider_data_layer,
{ "type": "FeatureLayer",
"renderer": renderer,
"definition_expression" : "user_taxonomy_code_1 in ('207V00000X','207VC0200X','207VF0040X','207VX0201X','207VG0400X','207VH0002X','207VM0101X','207VB0002X','207VX0000X','207VE0102X','363LX0001X','163WR1000X','163WW0101X','282NW0100X')"
# "definition_expression" : "user_taxonomy_code_1 = '207VC0200X' or user_taxonomy_code_1 = '207V00000X'"
})
# Remove Layer
women_map.remove_layers()
True
# Take Screenshot
women_map.take_screenshot()
Let's get data from the American Community Survey (ACS) about fertility in past 12 months by age of mother using ACS_Fertility_by_Age_Boundaries layer at State, County and Tract level. We will use 'Percent of women age 15 to 50 who had a birth in the past 12 months' as the variable to create our basemap and add providers as points on top.
# Get the Fertility Layers
fertility_search = gis.content.search('title: ACS_Fertility_by_Age_Boundaries', 'Feature Layer')
fertility_item = fertility_search[0]
fertility_item.layers
[<FeatureLayer url:"https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/0">, <FeatureLayer url:"https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/1">, <FeatureLayer url:"https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/2">]
# Define layers for State, County and Tract (Percent of women 15 to 50 who had a birth in the past 12 months)
fertility_state = fertility_item.layers[0]
fertility_county = fertility_item.layers[1]
fertility_tract = fertility_item.layers[2]
fertility_map = gis.map('USA')
fertility_map
From this map, we can see that percent of women (15 to 50) who had a birth in the past 12 months is high in Idaho, Utah, North Dakota, South Dakota and Nebraska.
# Add Fertility layers for State, County and Tract (Percent of women 15 to 50 who had a birth in the past 12 months)
from arcgis.mapping import MapImageLayer
fertility_map.add_layer(fertility_state,
{ "type": "FeatureLayer",
"field_name":"B13016_calc_pctBirthsE",
"renderer":"ClassedColorRenderer",
"opacity":0.5})
fertility_map.add_layer(fertility_county,
{ "type": "FeatureLayer",
"renderer":"ClassedColorRenderer",
"field_name":"B13016_calc_pctBirthsE",
"opacity":0.5})
fertility_map.add_layer(fertility_tract,
{ "type": "FeatureLayer",
"renderer":"ClassedColorRenderer",
"field_name":"B13016_calc_pctBirthsE",
"opacity":0.5})
# Add OB-GYN Provider Data
fertility_map.add_layer(provider_data_layer,
{"type":"FeatureLayer",
"definition_expression":"user_taxonomy_code_1 in ['207V00000X','207VC0200X','207VF0040X','207VX0201X','207VG0400X','207VH0002X','207VM0101X','207VB0002X','207VX0000X','207VE0102X','363LX0001X','163WR1000X','163WW0101X','282NW0100X']",
"opacity":0.7})
fertility_map.remove_layers()
True
fertility_map.legend = True
fertility_map.take_screenshot()
Let's find out the ratio of mothers to OBGYN providers to understand which states have the least number of OB-GYN providers
We will use the fertility layer at state level to create this dataframe
# State population dataframe
fertility_featureset = fertility_state.query(where="B13016_001E>1")
fertility_df = fertility_featureset.sdf
fertility_df.head()
| ALAND | AWATER | B13016_001E | B13016_001M | B13016_002E | B13016_002M | B13016_003E | B13016_003M | B13016_004E | B13016_004M | B13016_005E | B13016_005M | B13016_006E | B13016_006M | B13016_007E | B13016_007M | B13016_008E | B13016_008M | B13016_009E | B13016_009M | B13016_010E | B13016_010M | B13016_011E | B13016_011M | B13016_012E | B13016_012M | B13016_013E | B13016_013M | B13016_014E | B13016_014M | B13016_015E | B13016_015M | B13016_016E | B13016_016M | B13016_017E | B13016_017M | B13016_calc_num15to19E | B13016_calc_num15to19M | B13016_calc_num20to24E | B13016_calc_num20to24M | B13016_calc_num25to29E | B13016_calc_num25to29M | B13016_calc_num30to34E | B13016_calc_num30to34M | B13016_calc_num35to39E | B13016_calc_num35to39M | B13016_calc_num40to44E | B13016_calc_num40to44M | B13016_calc_num45to50E | B13016_calc_num45to50M | B13016_calc_pct15to19E | B13016_calc_pct15to19M | B13016_calc_pct20to24E | B13016_calc_pct20to24M | B13016_calc_pct25to29E | B13016_calc_pct25to29M | B13016_calc_pct30to34E | B13016_calc_pct30to34M | B13016_calc_pct35to39E | B13016_calc_pct35to39M | B13016_calc_pct40to44E | B13016_calc_pct40to44M | B13016_calc_pct45to50E | B13016_calc_pct45to50M | B13016_calc_pctBirthsE | B13016_calc_pctBirthsM | GEOID | NAME | OBJECTID | SHAPE | STATENS | STUSPS | Shape__Area | Shape__Length | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 131174431216 | 4592944701 | 1150523 | 1402 | 58732 | 1690 | 2903 | 415 | 14484 | 792 | 18605 | 889 | 13911 | 884 | 6318 | 680 | 1790 | 423 | 721 | 193 | 1091791 | 1867 | 157636 | 1096 | 152944 | 1175 | 144825 | 1120 | 141620 | 1125 | 147991 | 2099 | 152552 | 2185 | 194223 | 1366 | 160539 | 1172 | 167428 | 1417 | 163430 | 1430 | 155531 | 1431 | 154309 | 2206 | 154342 | 2226 | 194944 | 1380 | 1.8 | 0.258167 | 8.7 | 0.467339 | 11.4 | 0.534766 | 8.9 | 0.562386 | 4.1 | 0.436770 | 1.2 | 0.273556 | 0.4 | 0.098968 | 5.1 | 0.146698 | 01 | Alabama | 1 | {"rings": [[[-9805779.6231, 3536997.5778], [-9... | 01779775 | AL | 1.901226e+11 | 2.312416e+06 |
| 1 | 1478588231566 | 277723861311 | 174163 | 485 | 10582 | 662 | 408 | 125 | 2240 | 276 | 3190 | 359 | 2763 | 326 | 1483 | 270 | 430 | 99 | 68 | 40 | 163581 | 789 | 22202 | 326 | 22666 | 352 | 25803 | 411 | 23760 | 384 | 21123 | 710 | 21134 | 779 | 26893 | 421 | 22610 | 349 | 24906 | 447 | 28993 | 546 | 26523 | 504 | 22606 | 760 | 21564 | 785 | 26961 | 423 | 1.8 | 0.552151 | 9.0 | 1.096348 | 11.0 | 1.220770 | 10.4 | 1.213076 | 6.6 | 1.173833 | 2.0 | 0.453323 | 0.3 | 0.148310 | 6.1 | 0.379401 | 02 | Alaska | 2 | {"rings": [[[-19937057.4487, 6661392.0514], [-... | 01785533 | AK | 8.208934e+12 | 6.399688e+07 |
| 2 | 294198661567 | 1027245114 | 1571003 | 1527 | 85196 | 2315 | 3290 | 346 | 18209 | 982 | 24282 | 1113 | 22146 | 1110 | 12496 | 754 | 3273 | 324 | 1500 | 303 | 1485807 | 2427 | 222499 | 969 | 213748 | 1310 | 201156 | 1099 | 196251 | 1121 | 196215 | 2313 | 204729 | 2415 | 251209 | 1483 | 225789 | 1029 | 231957 | 1637 | 225438 | 1564 | 218397 | 1578 | 208711 | 2433 | 208002 | 2437 | 252709 | 1514 | 1.5 | 0.153096 | 7.9 | 0.419714 | 10.8 | 0.488018 | 10.1 | 0.502940 | 6.0 | 0.354459 | 1.6 | 0.154673 | 0.6 | 0.119848 | 5.4 | 0.147141 | 04 | Arizona | 3 | {"rings": [[[-12138854.2127, 4438965.0002], [-... | 01779777 | AZ | 4.340581e+11 | 2.906214e+06 |
| 3 | 134768100673 | 2963631791 | 689530 | 1063 | 40901 | 1606 | 2514 | 379 | 11224 | 869 | 12726 | 754 | 8852 | 676 | 4012 | 546 | 1088 | 240 | 485 | 148 | 648629 | 1983 | 95325 | 724 | 89233 | 1111 | 86020 | 996 | 86174 | 898 | 88668 | 1665 | 91183 | 1443 | 112026 | 960 | 97839 | 817 | 100457 | 1410 | 98746 | 1249 | 95026 | 1124 | 92680 | 1752 | 92271 | 1463 | 112511 | 971 | 2.6 | 0.386776 | 11.2 | 0.850713 | 12.9 | 0.745972 | 9.3 | 0.702799 | 4.3 | 0.583413 | 1.2 | 0.259431 | 0.4 | 0.131490 | 5.9 | 0.232502 | 05 | Arkansas | 4 | {"rings": [[[-9989043.0946, 4300705.9503], [-9... | 00068085 | AR | 2.053261e+11 | 2.656502e+06 |
| 4 | 403483182192 | 20484637928 | 9642845 | 3558 | 478458 | 5207 | 16079 | 831 | 76861 | 2027 | 120625 | 2303 | 138924 | 3037 | 87689 | 2301 | 28439 | 1168 | 9841 | 670 | 9164387 | 6546 | 1258668 | 1367 | 1302552 | 2268 | 1324982 | 2293 | 1245434 | 3141 | 1207288 | 5298 | 1256240 | 5262 | 1569223 | 3493 | 1274747 | 1600 | 1379413 | 3042 | 1445607 | 3250 | 1384358 | 4369 | 1294977 | 5776 | 1284679 | 5390 | 1579064 | 3557 | 1.3 | 0.065170 | 5.6 | 0.146432 | 8.3 | 0.158202 | 10.0 | 0.217082 | 6.8 | 0.175101 | 2.2 | 0.090442 | 0.6 | 0.042407 | 5.0 | 0.053932 | 06 | California | 5 | {"rings": [[[-13198544.1255, 3897778.6019], [-... | 01779778 | CA | 6.500881e+11 | 5.274852e+06 |
We will use provider_data_layer to subset OB-GYN providers
# Get provider data for obgyn providers only
obgyn_featureset = provider_data_layer.query(where="user_taxonomy_code_1 in ('207V00000X','207VC0200X','207VF0040X','207VX0201X','207VG0400X','207VH0002X','207VM0101X','207VB0002X','207VX0000X','207VE0102X','363LX0001X','163WR1000X','163WW0101X','282NW0100X')", out_fields='user_npi,user_entity_type,user_provider_gender,user_taxonomy_code_1,user_full_address,postal,city,subregion,region,regionabbr')
obgyn_df = obgyn_featureset.sdf
obgyn_df.head()
| SHAPE | city | objectid | postal | region | regionabbr | subregion | user_entity_type | user_full_address | user_npi | user_provider_gender | user_taxonomy_code_1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | {"x": -97.49116252599998, "y": 35.261905469000... | Norman | 8 | 73072 | Oklahoma | OK | Cleveland County | Individual | 3400 W TECUMSEH RD, SUITE 205, NORMAN, OK 7307... | 1.487658e+09 | F | 207V00000X |
| 1 | {"x": -120.00990533799995, "y": 46.31641132900... | Sunnyside | 24 | 98944 | Washington | WA | Yakima County | Individual | 803 E LINCOLN AVE, , SUNNYSIDE, WA 989442383 | 1.265435e+09 | F | 207V00000X |
| 2 | {"x": -81.63739261199999, "y": 38.359802984000... | Charleston | 26 | 25302 | West Virginia | WV | Kanawha County | Individual | 830 PENNSYLVANIA AVE, SUITE 108, CHARLESTON, W... | 1.447253e+09 | M | 207V00000X |
| 3 | {"x": -81.63739261199999, "y": 38.359802984000... | Charleston | 99 | 25302 | West Virginia | WV | Kanawha County | Individual | 830 PENNSYLVANIA AVE, STE 402, CHARLESTON, WV ... | 1.487658e+09 | M | 207V00000X |
| 4 | {"x": -81.63739261199999, "y": 38.359802984000... | Charleston | 172 | 25302 | West Virginia | WV | Kanawha County | Individual | 830 PENNSYLVANIA AVE, STE 402, CHARLESTON, WV ... | 1.255334e+09 | M | 207V00000X |
# Create dataframe of obgyn provider counts by state
obgyn_count_df = pd.DataFrame(obgyn_df['regionabbr'].value_counts().reset_index().values, columns=['regionabbr','Provider_Count'])
# Plot OB-GYN Providers by State
plt.figure(figsize=(25,12))
sns.barplot(obgyn_count_df['regionabbr'].iloc[:-8], obgyn_count_df['Provider_Count'].iloc[:-8])
plt.title('OB-GYN Providers by State', fontsize=22)
plt.xlabel('States', fontsize=18)
plt.ylabel('Provider Count', fontsize=18)
Text(0, 0.5, 'Provider Count')
# Merge provider count and women_df at state level
state_obgyn_df = pd.merge(obgyn_count_df,fertility_df,left_on='regionabbr', right_on='STUSPS',how='inner')
# Create new columns that shows provider by women pop
state_obgyn_df['women_per_prov'] = state_obgyn_df['B13016_001E']/state_obgyn_df['Provider_Count']
# Create new columns that shows provider by mother pop
state_obgyn_df['mother_per_prov'] = state_obgyn_df['B13016_002E']/state_obgyn_df['Provider_Count']
# Arrange dataframe by mother_per_prov descending
state_obgyn_df = state_obgyn_df.sort_values(by=['mother_per_prov'], ascending=False)
state_obgyn_df
| regionabbr | Provider_Count | ALAND | AWATER | B13016_001E | B13016_001M | B13016_002E | B13016_002M | B13016_003E | B13016_003M | B13016_004E | B13016_004M | B13016_005E | B13016_005M | B13016_006E | B13016_006M | B13016_007E | B13016_007M | B13016_008E | B13016_008M | B13016_009E | B13016_009M | B13016_010E | B13016_010M | B13016_011E | B13016_011M | B13016_012E | B13016_012M | B13016_013E | B13016_013M | B13016_014E | B13016_014M | B13016_015E | B13016_015M | B13016_016E | B13016_016M | B13016_017E | B13016_017M | B13016_calc_num15to19E | B13016_calc_num15to19M | B13016_calc_num20to24E | B13016_calc_num20to24M | B13016_calc_num25to29E | B13016_calc_num25to29M | B13016_calc_num30to34E | B13016_calc_num30to34M | B13016_calc_num35to39E | B13016_calc_num35to39M | B13016_calc_num40to44E | B13016_calc_num40to44M | B13016_calc_num45to50E | B13016_calc_num45to50M | B13016_calc_pct15to19E | B13016_calc_pct15to19M | B13016_calc_pct20to24E | B13016_calc_pct20to24M | B13016_calc_pct25to29E | B13016_calc_pct25to29M | B13016_calc_pct30to34E | B13016_calc_pct30to34M | B13016_calc_pct35to39E | B13016_calc_pct35to39M | B13016_calc_pct40to44E | B13016_calc_pct40to44M | B13016_calc_pct45to50E | B13016_calc_pct45to50M | B13016_calc_pctBirthsE | B13016_calc_pctBirthsM | GEOID | NAME | OBJECTID | SHAPE | STATENS | STUSPS | Shape__Area | Shape__Length | women_per_prov | mother_per_prov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | ND | 102 | 178710423375 | 4400396641 | 168792 | 563 | 11410 | 783 | 517 | 161 | 2609 | 459 | 3726 | 398 | 2880 | 363 | 1411 | 359 | 211 | 79 | 56 | 39 | 157382 | 957 | 22304 | 415 | 28639 | 582 | 22947 | 498 | 21004 | 401 | 19288 | 789 | 18822 | 685 | 24378 | 456 | 22821 | 445 | 31248 | 741 | 26673 | 638 | 23884 | 541 | 20699 | 867 | 19033 | 690 | 24434 | 458 | 2.3 | 0.704106 | 8.3 | 1.455489 | 14.0 | 1.454254 | 12.1 | 1.495102 | 6.8 | 1.710719 | 1.1 | 0.413118 | 0.2 | 0.159556 | 6.8 | 0.462823 | 38 | North Dakota | 35 | {'rings': [[[-10823452.8858, 6274958.149], [-1... | 01779797 | ND | 4.003510e+11 | 3.123593e+06 | 1654.82 | 111.863 |
| 32 | UT | 526 | 212884846341 | 7000199770 | 744748 | 957 | 52639 | 1428 | 1263 | 256 | 10387 | 710 | 16697 | 866 | 15192 | 712 | 7502 | 531 | 1267 | 267 | 331 | 133 | 692109 | 1759 | 112478 | 509 | 112858 | 819 | 92431 | 832 | 94332 | 748 | 98897 | 1460 | 87738 | 1541 | 93375 | 840 | 113741 | 570 | 123245 | 1084 | 109128 | 1201 | 109524 | 1033 | 106399 | 1554 | 89005 | 1564 | 93706 | 850 | 1.1 | 0.225004 | 8.4 | 0.571299 | 15.3 | 0.775493 | 13.9 | 0.636786 | 7.1 | 0.488325 | 1.4 | 0.298938 | 0.4 | 0.141897 | 7.1 | 0.191263 | 49 | Utah | 45 | {'rings': [[[-12139394.6091, 5012444.5138], [-... | 01455989 | UT | 3.682861e+11 | 2.557183e+06 | 1415.87 | 100.074 |
| 35 | AR | 440 | 134768100673 | 2963631791 | 689530 | 1063 | 40901 | 1606 | 2514 | 379 | 11224 | 869 | 12726 | 754 | 8852 | 676 | 4012 | 546 | 1088 | 240 | 485 | 148 | 648629 | 1983 | 95325 | 724 | 89233 | 1111 | 86020 | 996 | 86174 | 898 | 88668 | 1665 | 91183 | 1443 | 112026 | 960 | 97839 | 817 | 100457 | 1410 | 98746 | 1249 | 95026 | 1124 | 92680 | 1752 | 92271 | 1463 | 112511 | 971 | 2.6 | 0.386776 | 11.2 | 0.850713 | 12.9 | 0.745972 | 9.3 | 0.702799 | 4.3 | 0.583413 | 1.2 | 0.259431 | 0.4 | 0.131490 | 5.9 | 0.232502 | 05 | Arkansas | 4 | {'rings': [[[-9989043.0946, 4300705.9503], [-9... | 00068085 | AR | 2.053261e+11 | 2.656502e+06 | 1567.11 | 92.9568 |
| 49 | SD | 137 | 196345228871 | 3384471850 | 186754 | 651 | 12697 | 717 | 518 | 138 | 2940 | 319 | 3849 | 413 | 3346 | 358 | 1581 | 259 | 326 | 121 | 137 | 75 | 174057 | 958 | 26460 | 431 | 26086 | 538 | 23172 | 488 | 23773 | 446 | 23415 | 710 | 22287 | 652 | 28864 | 519 | 26978 | 453 | 29026 | 625 | 27021 | 639 | 27119 | 572 | 24996 | 756 | 22613 | 663 | 29001 | 524 | 1.9 | 0.510511 | 10.1 | 1.077157 | 14.2 | 1.490858 | 12.3 | 1.294202 | 6.3 | 1.018354 | 1.4 | 0.533418 | 0.5 | 0.258471 | 6.8 | 0.383039 | 46 | South Dakota | 42 | {'rings': [[[-10749419.778, 5769979.5139], [-1... | 01785534 | SD | 3.919820e+11 | 2.962929e+06 | 1363.17 | 92.6788 |
| 43 | ID | 250 | 214048160737 | 2393355752 | 376343 | 997 | 22877 | 960 | 810 | 230 | 5132 | 512 | 7280 | 635 | 6059 | 590 | 2698 | 402 | 614 | 197 | 284 | 118 | 353466 | 1221 | 56534 | 619 | 48332 | 641 | 45833 | 703 | 47302 | 723 | 49003 | 1372 | 48670 | 1344 | 57792 | 862 | 57344 | 660 | 53464 | 820 | 53113 | 947 | 53361 | 933 | 51701 | 1430 | 49284 | 1358 | 58076 | 870 | 1.4 | 0.400759 | 9.6 | 0.946269 | 13.7 | 1.170320 | 11.4 | 1.087706 | 5.2 | 0.764034 | 1.2 | 0.398247 | 0.5 | 0.203050 | 6.1 | 0.254615 | 16 | Idaho | 13 | {'rings': [[[-12918526.7316, 6275006.0277], [-... | 01779783 | ID | 4.246748e+11 | 4.197354e+06 | 1505.37 | 91.508 |
| 34 | IA | 480 | 144664158135 | 1081293682 | 701974 | 1000 | 41743 | 1604 | 1689 | 243 | 8619 | 825 | 12653 | 687 | 11690 | 657 | 5427 | 509 | 1084 | 202 | 581 | 153 | 660231 | 1735 | 102593 | 513 | 102818 | 1014 | 81504 | 832 | 85354 | 784 | 87584 | 1311 | 87550 | 1343 | 112828 | 900 | 104282 | 568 | 111437 | 1307 | 94157 | 1079 | 97044 | 1023 | 93011 | 1406 | 88634 | 1358 | 113409 | 913 | 1.6 | 0.232855 | 7.7 | 0.734750 | 13.4 | 0.713196 | 12.0 | 0.664997 | 5.8 | 0.540092 | 1.2 | 0.227132 | 0.5 | 0.134847 | 5.9 | 0.228094 | 19 | Iowa | 16 | {'rings': [[[-10154309.3595, 5388474.8922], [-... | 01779785 | IA | 2.648094e+11 | 2.527131e+06 | 1462.45 | 86.9646 |
| 38 | NE | 336 | 198957965731 | 1370523694 | 432593 | 701 | 27898 | 932 | 730 | 171 | 5463 | 461 | 8225 | 523 | 8611 | 542 | 3585 | 354 | 814 | 147 | 470 | 140 | 404695 | 1042 | 62040 | 499 | 61378 | 681 | 52108 | 580 | 54877 | 587 | 54908 | 1025 | 53435 | 1002 | 65949 | 606 | 62770 | 527 | 66841 | 822 | 60333 | 781 | 63488 | 799 | 58493 | 1084 | 54249 | 1013 | 66419 | 622 | 1.2 | 0.272248 | 8.2 | 0.682333 | 13.6 | 0.848703 | 13.6 | 0.836466 | 6.1 | 0.594447 | 1.5 | 0.269520 | 0.7 | 0.210679 | 6.4 | 0.214997 | 31 | Nebraska | 28 | {'rings': [[[-10736262.9814, 5234760.2573], [-... | 01779792 | NE | 3.578638e+11 | 2.922099e+06 | 1287.48 | 83.0298 |
| 33 | KS | 486 | 211753159949 | 1346325556 | 662453 | 1061 | 39414 | 1429 | 1687 | 279 | 8906 | 730 | 10899 | 674 | 11480 | 653 | 4970 | 483 | 1053 | 181 | 419 | 116 | 623039 | 1549 | 95455 | 743 | 93872 | 976 | 81579 | 911 | 83346 | 770 | 82588 | 1355 | 82948 | 1302 | 103251 | 848 | 97142 | 794 | 102778 | 1219 | 92478 | 1133 | 94826 | 1010 | 87558 | 1439 | 84001 | 1315 | 103670 | 856 | 1.7 | 0.286857 | 8.7 | 0.702794 | 11.8 | 0.714376 | 12.1 | 0.676449 | 5.7 | 0.543689 | 1.3 | 0.214578 | 0.4 | 0.111844 | 5.9 | 0.215331 | 20 | Kansas | 17 | {'rings': [[[-10609671.1704, 4865943.0191], [-... | 00481813 | KS | 3.484442e+11 | 2.570743e+06 | 1363.07 | 81.0988 |
| 28 | OK | 668 | 177664450004 | 3373087228 | 904535 | 884 | 53590 | 1422 | 2937 | 286 | 14103 | 760 | 15571 | 816 | 12508 | 702 | 6145 | 454 | 1550 | 222 | 776 | 154 | 850945 | 1533 | 122870 | 616 | 122845 | 799 | 117785 | 877 | 119076 | 803 | 111910 | 1381 | 117202 | 1377 | 139257 | 887 | 125807 | 679 | 136948 | 1103 | 133356 | 1198 | 131584 | 1067 | 118055 | 1454 | 118752 | 1395 | 140033 | 900 | 2.3 | 0.226983 | 10.3 | 0.548722 | 11.7 | 0.602838 | 9.5 | 0.527902 | 5.2 | 0.379185 | 1.3 | 0.186314 | 0.6 | 0.109916 | 5.9 | 0.156932 | 40 | Oklahoma | 37 | {'rings': [[[-10532824.3805, 4438955.0818], [-... | 01102857 | OK | 2.744190e+11 | 3.307886e+06 | 1354.09 | 80.2246 |
| 24 | MN | 932 | 206229176104 | 18944967530 | 1268872 | 1143 | 73295 | 1732 | 2101 | 293 | 11193 | 595 | 19565 | 807 | 25081 | 941 | 11248 | 605 | 3000 | 324 | 1107 | 210 | 1195577 | 1943 | 172421 | 645 | 167175 | 694 | 159438 | 903 | 164182 | 998 | 159460 | 2115 | 160590 | 2161 | 212311 | 1075 | 174522 | 708 | 178368 | 914 | 179003 | 1211 | 189263 | 1372 | 170708 | 2200 | 163590 | 2185 | 213418 | 1095 | 1.2 | 0.167816 | 6.3 | 0.332027 | 10.9 | 0.444725 | 13.3 | 0.487823 | 6.6 | 0.344083 | 1.8 | 0.196536 | 0.5 | 0.098362 | 5.8 | 0.136271 | 27 | Minnesota | 24 | {'rings': [[[-10243096.0782, 5894294.6155], [-... | 00662849 | MN | 4.587742e+11 | 4.504564e+06 | 1361.45 | 78.6427 |
| 1 | TX | 5161 | 676641930188 | 19017521093 | 6800021 | 2997 | 397530 | 5500 | 21768 | 1195 | 86594 | 2398 | 109234 | 2557 | 104129 | 2933 | 54463 | 1770 | 16076 | 926 | 5266 | 497 | 6402491 | 6428 | 930609 | 1869 | 872706 | 2713 | 878353 | 2601 | 878663 | 3357 | 876401 | 5195 | 907688 | 5045 | 1058071 | 2829 | 952377 | 2218 | 959300 | 3621 | 987587 | 3647 | 982792 | 4458 | 930864 | 5488 | 923764 | 5129 | 1063337 | 2872 | 2.3 | 0.125363 | 9.0 | 0.247641 | 11.1 | 0.255672 | 10.6 | 0.294540 | 5.9 | 0.186991 | 1.7 | 0.099775 | 0.5 | 0.046721 | 5.8 | 0.080744 | 48 | Texas | 44 | {'rings': [[[-10867517.6923, 2998334.6597], [-... | 01779801 | TX | 9.487726e+11 | 8.498835e+06 | 1317.58 | 77.0258 |
| 23 | WI | 934 | 140275464079 | 29359527964 | 1312355 | 1160 | 67517 | 1511 | 1782 | 296 | 12926 | 751 | 18740 | 762 | 21201 | 784 | 9297 | 582 | 2589 | 320 | 982 | 188 | 1244838 | 1825 | 185801 | 643 | 185287 | 805 | 155085 | 871 | 161462 | 863 | 163691 | 1828 | 166713 | 1668 | 226799 | 1102 | 187583 | 708 | 198213 | 1101 | 173825 | 1157 | 182663 | 1166 | 172988 | 1918 | 169302 | 1698 | 227781 | 1118 | 0.9 | 0.157756 | 6.5 | 0.377150 | 10.8 | 0.432459 | 11.6 | 0.422763 | 5.4 | 0.331121 | 1.5 | 0.188388 | 0.4 | 0.082508 | 5.1 | 0.114984 | 55 | Wisconsin | 50 | {'rings': [[[-9739359.7332, 5630191.737], [-97... | 01779806 | WI | 2.874128e+11 | 3.531267e+06 | 1405.09 | 72.288 |
| 30 | MS | 530 | 121530256608 | 3930182746 | 714792 | 1225 | 37818 | 1534 | 2383 | 361 | 10517 | 674 | 11817 | 764 | 7666 | 635 | 3786 | 422 | 1000 | 222 | 649 | 206 | 676974 | 2173 | 102332 | 1294 | 95368 | 1337 | 88387 | 878 | 89783 | 809 | 91519 | 1561 | 93499 | 1668 | 116086 | 1046 | 104715 | 1343 | 105885 | 1497 | 100204 | 1164 | 97449 | 1028 | 95305 | 1617 | 94499 | 1683 | 116735 | 1066 | 2.3 | 0.343508 | 9.9 | 0.620857 | 11.8 | 0.750037 | 7.9 | 0.646317 | 4.0 | 0.437629 | 1.1 | 0.234166 | 0.6 | 0.176395 | 5.3 | 0.214307 | 28 | Mississippi | 25 | {'rings': [[[-9847958.7008, 3529611.841], [-98... | 01779790 | MS | 1.752353e+11 | 3.003227e+06 | 1348.66 | 71.3547 |
| 26 | AL | 831 | 131174431216 | 4592944701 | 1150523 | 1402 | 58732 | 1690 | 2903 | 415 | 14484 | 792 | 18605 | 889 | 13911 | 884 | 6318 | 680 | 1790 | 423 | 721 | 193 | 1091791 | 1867 | 157636 | 1096 | 152944 | 1175 | 144825 | 1120 | 141620 | 1125 | 147991 | 2099 | 152552 | 2185 | 194223 | 1366 | 160539 | 1172 | 167428 | 1417 | 163430 | 1430 | 155531 | 1431 | 154309 | 2206 | 154342 | 2226 | 194944 | 1380 | 1.8 | 0.258167 | 8.7 | 0.467339 | 11.4 | 0.534766 | 8.9 | 0.562386 | 4.1 | 0.436770 | 1.2 | 0.273556 | 0.4 | 0.098968 | 5.1 | 0.146698 | 01 | Alabama | 1 | {'rings': [[[-9805779.6231, 3536997.5778], [-9... | 01779775 | AL | 1.901226e+11 | 2.312416e+06 | 1384.5 | 70.6763 |
| 14 | WA | 1304 | 172111800165 | 12560067439 | 1700267 | 1488 | 92040 | 2034 | 2729 | 392 | 15476 | 1058 | 25580 | 1155 | 28015 | 975 | 15442 | 943 | 3270 | 360 | 1528 | 268 | 1608227 | 2552 | 211659 | 736 | 216767 | 1102 | 230636 | 1166 | 226631 | 980 | 221964 | 2666 | 221665 | 2536 | 278905 | 1447 | 214388 | 834 | 232243 | 1528 | 256216 | 1641 | 254646 | 1382 | 237406 | 2828 | 224935 | 2561 | 280433 | 1472 | 1.3 | 0.182779 | 6.7 | 0.453443 | 10.0 | 0.446233 | 11.0 | 0.378200 | 6.5 | 0.389580 | 1.5 | 0.159188 | 0.5 | 0.095524 | 5.4 | 0.119453 | 53 | Washington | 48 | {'rings': [[[-13651702.4638, 5966299.4185], [-... | 01779804 | WA | 3.831213e+11 | 5.712577e+06 | 1303.89 | 70.5828 |
| 47 | AK | 155 | 1478588231566 | 277723861311 | 174163 | 485 | 10582 | 662 | 408 | 125 | 2240 | 276 | 3190 | 359 | 2763 | 326 | 1483 | 270 | 430 | 99 | 68 | 40 | 163581 | 789 | 22202 | 326 | 22666 | 352 | 25803 | 411 | 23760 | 384 | 21123 | 710 | 21134 | 779 | 26893 | 421 | 22610 | 349 | 24906 | 447 | 28993 | 546 | 26523 | 504 | 22606 | 760 | 21564 | 785 | 26961 | 423 | 1.8 | 0.552151 | 9.0 | 1.096348 | 11.0 | 1.220770 | 10.4 | 1.213076 | 6.6 | 1.173833 | 2.0 | 0.453323 | 0.3 | 0.148310 | 6.1 | 0.379401 | 02 | Alaska | 2 | {'rings': [[[-19937057.4487, 6661392.0514], [-... | 01785533 | AK | 8.208934e+12 | 6.399688e+07 | 1123.63 | 68.271 |
| 18 | IN | 1226 | 92787765193 | 1539541677 | 1552714 | 1801 | 83517 | 2136 | 3805 | 417 | 19285 | 1076 | 23972 | 1055 | 22734 | 1091 | 9433 | 681 | 3196 | 366 | 1092 | 204 | 1469197 | 2803 | 220438 | 998 | 215755 | 1343 | 189329 | 1208 | 187767 | 1234 | 197144 | 2121 | 201249 | 2184 | 257515 | 1595 | 224243 | 1082 | 235040 | 1721 | 213301 | 1604 | 210501 | 1647 | 206577 | 2228 | 204445 | 2214 | 258607 | 1608 | 1.7 | 0.185779 | 8.2 | 0.453835 | 11.2 | 0.487332 | 10.8 | 0.511352 | 4.6 | 0.325960 | 1.6 | 0.178219 | 0.4 | 0.078840 | 5.4 | 0.137366 | 18 | Indiana | 15 | {'rings': [[[-9440558.1637, 5115568.7167], [-9... | 00448508 | IN | 1.595776e+11 | 2.230325e+06 | 1266.49 | 68.1215 |
| 16 | AZ | 1257 | 294198661567 | 1027245114 | 1571003 | 1527 | 85196 | 2315 | 3290 | 346 | 18209 | 982 | 24282 | 1113 | 22146 | 1110 | 12496 | 754 | 3273 | 324 | 1500 | 303 | 1485807 | 2427 | 222499 | 969 | 213748 | 1310 | 201156 | 1099 | 196251 | 1121 | 196215 | 2313 | 204729 | 2415 | 251209 | 1483 | 225789 | 1029 | 231957 | 1637 | 225438 | 1564 | 218397 | 1578 | 208711 | 2433 | 208002 | 2437 | 252709 | 1514 | 1.5 | 0.153096 | 7.9 | 0.419714 | 10.8 | 0.488018 | 10.1 | 0.502940 | 6.0 | 0.354459 | 1.6 | 0.154673 | 0.6 | 0.119848 | 5.4 | 0.147141 | 04 | Arizona | 3 | {'rings': [[[-12138854.2127, 4438965.0002], [-... | 01779777 | AZ | 4.340581e+11 | 2.906214e+06 | 1249.8 | 67.7772 |
| 31 | NV | 530 | 284329327067 | 2047350803 | 688113 | 933 | 35640 | 1268 | 1283 | 226 | 6223 | 592 | 10631 | 732 | 9530 | 608 | 5524 | 525 | 1890 | 307 | 559 | 152 | 652473 | 1553 | 84103 | 463 | 84252 | 577 | 93345 | 773 | 91894 | 702 | 89600 | 1600 | 94203 | 1543 | 115076 | 842 | 85386 | 515 | 90475 | 827 | 103976 | 1065 | 101424 | 929 | 95124 | 1684 | 96093 | 1573 | 115635 | 856 | 1.5 | 0.264525 | 6.9 | 0.651297 | 10.2 | 0.696176 | 9.4 | 0.593253 | 5.8 | 0.542252 | 2.0 | 0.317856 | 0.5 | 0.131399 | 5.2 | 0.184025 | 32 | Nevada | 29 | {'rings': [[[-12695040.5225, 5160062.591], [-1... | 01779793 | NV | 4.802969e+11 | 3.056062e+06 | 1298.33 | 67.2453 |
| 50 | WY | 110 | 251465641446 | 1860628076 | 129212 | 681 | 7365 | 602 | 375 | 126 | 1544 | 305 | 2356 | 304 | 2043 | 304 | 671 | 159 | 182 | 91 | 194 | 128 | 121847 | 800 | 17167 | 389 | 17492 | 455 | 16875 | 469 | 17498 | 370 | 17010 | 743 | 16377 | 706 | 19428 | 396 | 17542 | 409 | 19036 | 548 | 19231 | 559 | 19541 | 479 | 17681 | 760 | 16559 | 712 | 19622 | 416 | 2.1 | 0.716545 | 8.1 | 1.585122 | 12.3 | 1.540148 | 10.5 | 1.534449 | 3.8 | 0.884351 | 1.1 | 0.547514 | 1.0 | 0.651992 | 5.7 | 0.465144 | 56 | Wyoming | 51 | {'rings': [[[-11583671.286, 5621145.5785], [-1... | 01779807 | WY | 4.741976e+11 | 2.777154e+06 | 1174.65 | 66.9545 |
| 25 | KY | 838 | 102266092821 | 2388731561 | 1030561 | 1405 | 55402 | 1750 | 3028 | 399 | 13166 | 857 | 16961 | 1022 | 13590 | 776 | 6097 | 564 | 1600 | 249 | 960 | 204 | 975159 | 2193 | 138223 | 959 | 135326 | 1058 | 126513 | 1129 | 126042 | 899 | 132526 | 2176 | 138214 | 2165 | 178315 | 1333 | 141251 | 1039 | 148492 | 1362 | 143474 | 1523 | 139632 | 1188 | 138623 | 2248 | 139814 | 2179 | 179275 | 1349 | 2.1 | 0.282035 | 8.9 | 0.571377 | 11.8 | 0.701184 | 9.7 | 0.549543 | 4.4 | 0.400558 | 1.1 | 0.177198 | 0.5 | 0.113720 | 5.4 | 0.169565 | 21 | Kentucky | 18 | {'rings': [[[-9961457.8678, 4369322.1272], [-9... | 01779786 | KY | 1.667122e+11 | 2.753667e+06 | 1229.79 | 66.1122 |
| 17 | TN | 1247 | 106798015774 | 2354836197 | 1568704 | 1693 | 80791 | 1690 | 4526 | 524 | 20304 | 905 | 23108 | 1028 | 20814 | 977 | 8840 | 612 | 2353 | 352 | 846 | 179 | 1487913 | 2450 | 201325 | 1026 | 205419 | 1273 | 203950 | 1157 | 195684 | 1206 | 202771 | 2291 | 210101 | 2246 | 268663 | 1487 | 205851 | 1152 | 225723 | 1562 | 227058 | 1548 | 216498 | 1552 | 211611 | 2371 | 212454 | 2273 | 269509 | 1498 | 2.2 | 0.254255 | 9.0 | 0.396073 | 10.2 | 0.447400 | 9.6 | 0.445981 | 4.2 | 0.285397 | 1.1 | 0.165259 | 0.3 | 0.066394 | 5.2 | 0.107589 | 47 | Tennessee | 43 | {'rings': [[[-9314702.8266, 4383607.3303], [-9... | 01325873 | TN | 1.664791e+11 | 2.621414e+06 | 1257.98 | 64.7883 |
| 36 | NM | 410 | 314191415563 | 733669653 | 473337 | 885 | 26525 | 1011 | 1668 | 320 | 6652 | 660 | 7358 | 493 | 6006 | 493 | 3241 | 368 | 1069 | 264 | 531 | 147 | 446812 | 1265 | 66249 | 597 | 65036 | 722 | 60420 | 529 | 61432 | 543 | 57947 | 1392 | 60040 | 1326 | 75688 | 914 | 67917 | 677 | 71688 | 978 | 67778 | 723 | 67438 | 733 | 61188 | 1440 | 61109 | 1352 | 76219 | 926 | 2.5 | 0.470527 | 9.3 | 0.911912 | 10.9 | 0.718097 | 8.9 | 0.724605 | 5.3 | 0.588365 | 1.7 | 0.430278 | 0.7 | 0.192679 | 5.6 | 0.213254 | 35 | New Mexico | 32 | {'rings': [[[-11466167.8568, 4439128.5521], [-... | 00897535 | NM | 4.644013e+11 | 2.900272e+06 | 1154.48 | 64.6951 |
| 22 | LA | 950 | 111904803121 | 23746413153 | 1118915 | 1237 | 60549 | 1822 | 3272 | 477 | 14926 | 787 | 18139 | 1093 | 15227 | 1015 | 6641 | 592 | 1463 | 224 | 881 | 239 | 1058366 | 2319 | 143686 | 845 | 152617 | 1005 | 153462 | 1154 | 149438 | 1227 | 140645 | 1983 | 140142 | 2076 | 178376 | 1105 | 146958 | 970 | 167543 | 1276 | 171601 | 1589 | 164665 | 1592 | 147286 | 2069 | 141605 | 2088 | 179257 | 1131 | 2.2 | 0.324250 | 8.9 | 0.464804 | 10.6 | 0.629377 | 9.2 | 0.609885 | 4.5 | 0.396917 | 1.0 | 0.157451 | 0.5 | 0.133292 | 5.4 | 0.162598 | 22 | Louisiana | 19 | {'rings': [[[-10120798.2502, 3381965.1428], [-... | 01629543 | LA | 1.675793e+11 | 4.198750e+06 | 1177.81 | 63.7358 |
| 39 | WV | 310 | 62265662566 | 489840834 | 402606 | 842 | 19682 | 946 | 962 | 193 | 5270 | 530 | 6147 | 516 | 4683 | 490 | 1758 | 262 | 474 | 136 | 388 | 133 | 382924 | 1448 | 52229 | 589 | 54109 | 734 | 47390 | 658 | 48286 | 560 | 52550 | 1252 | 56801 | 1174 | 71559 | 710 | 53191 | 620 | 59379 | 905 | 53537 | 836 | 52969 | 744 | 54308 | 1279 | 57275 | 1182 | 71947 | 722 | 1.8 | 0.362230 | 8.9 | 0.882262 | 11.5 | 0.946996 | 8.8 | 0.916697 | 3.2 | 0.476372 | 0.8 | 0.236836 | 0.5 | 0.184779 | 4.9 | 0.234688 | 54 | West Virginia | 49 | {'rings': [[[-8847301.8894, 4825493.6305], [-8... | 01779805 | WV | 1.030460e+11 | 2.614073e+06 | 1298.73 | 63.4903 |
| 0 | CA | 7543 | 403483182192 | 20484637928 | 9642845 | 3558 | 478458 | 5207 | 16079 | 831 | 76861 | 2027 | 120625 | 2303 | 138924 | 3037 | 87689 | 2301 | 28439 | 1168 | 9841 | 670 | 9164387 | 6546 | 1258668 | 1367 | 1302552 | 2268 | 1324982 | 2293 | 1245434 | 3141 | 1207288 | 5298 | 1256240 | 5262 | 1569223 | 3493 | 1274747 | 1600 | 1379413 | 3042 | 1445607 | 3250 | 1384358 | 4369 | 1294977 | 5776 | 1284679 | 5390 | 1579064 | 3557 | 1.3 | 0.065170 | 5.6 | 0.146432 | 8.3 | 0.158202 | 10.0 | 0.217082 | 6.8 | 0.175101 | 2.2 | 0.090442 | 0.6 | 0.042407 | 5.0 | 0.053932 | 06 | California | 5 | {'rings': [[[-13198544.1255, 3897778.6019], [-... | 01779778 | CA | 6.500881e+11 | 5.274852e+06 | 1278.38 | 63.4307 |
| 45 | ME | 226 | 79885808421 | 11748168571 | 289915 | 724 | 14100 | 799 | 336 | 101 | 2505 | 379 | 3953 | 437 | 4730 | 494 | 1795 | 266 | 566 | 163 | 215 | 83 | 275815 | 1171 | 39289 | 411 | 35635 | 529 | 34673 | 431 | 33882 | 553 | 35424 | 773 | 40593 | 827 | 56319 | 669 | 39625 | 423 | 38140 | 651 | 38626 | 614 | 38612 | 742 | 37219 | 817 | 41159 | 843 | 56534 | 674 | 0.8 | 0.254729 | 6.6 | 0.987364 | 10.2 | 1.119605 | 12.3 | 1.257551 | 4.8 | 0.706804 | 1.4 | 0.395022 | 0.4 | 0.146744 | 4.9 | 0.275272 | 23 | Maine | 20 | {'rings': [[[-7859306.2593, 5309094.2568], [-7... | 01779787 | ME | 1.723955e+11 | 3.775975e+06 | 1282.81 | 62.3894 |
| 7 | OH | 2303 | 105833282399 | 10264451012 | 2667220 | 1781 | 141773 | 2541 | 6130 | 524 | 30094 | 1354 | 42334 | 1574 | 38506 | 1191 | 18285 | 966 | 4320 | 414 | 2104 | 282 | 2525447 | 2850 | 371578 | 986 | 355390 | 1582 | 334827 | 1619 | 322728 | 1436 | 327887 | 2534 | 352679 | 2623 | 460358 | 1495 | 377708 | 1117 | 385484 | 2082 | 377161 | 2258 | 361234 | 1866 | 346172 | 2712 | 356999 | 2655 | 462462 | 1521 | 1.6 | 0.138648 | 7.8 | 0.348707 | 11.2 | 0.411883 | 10.7 | 0.325073 | 5.3 | 0.275967 | 1.2 | 0.115617 | 0.5 | 0.060960 | 5.3 | 0.095133 | 39 | Ohio | 36 | {'rings': [[[-9206145.5154, 5103064.4505], [-9... | 01085497 | OH | 1.842500e+11 | 2.145534e+06 | 1158.15 | 61.5601 |
| 9 | GA | 2221 | 149177524294 | 4733385577 | 2555460 | 2228 | 136101 | 2966 | 6976 | 689 | 29979 | 1407 | 37397 | 1708 | 34265 | 1749 | 19692 | 1085 | 5484 | 537 | 2308 | 359 | 2419359 | 3653 | 342872 | 1735 | 321174 | 1978 | 319620 | 1873 | 318105 | 2028 | 332176 | 4063 | 351277 | 3863 | 434135 | 2063 | 349848 | 1867 | 351153 | 2427 | 357017 | 2535 | 352370 | 2678 | 351868 | 4205 | 356761 | 3900 | 436443 | 2094 | 2.0 | 0.196655 | 8.5 | 0.396312 | 10.5 | 0.472592 | 9.7 | 0.490821 | 5.6 | 0.301014 | 1.5 | 0.149580 | 0.5 | 0.082217 | 5.3 | 0.115900 | 13 | Georgia | 11 | {'rings': [[[-9251608.9138, 4163971.617], [-92... | 01705317 | GA | 2.159941e+11 | 2.582918e+06 | 1150.59 | 61.2792 |
| 11 | VA | 1752 | 102254240128 | 8532012665 | 2030818 | 1520 | 105067 | 2534 | 3410 | 426 | 17561 | 983 | 27855 | 1074 | 31174 | 1228 | 17769 | 907 | 5313 | 488 | 1985 | 313 | 1925751 | 3050 | 265171 | 1434 | 264803 | 1720 | 261294 | 1297 | 255919 | 1537 | 257606 | 2676 | 270990 | 2501 | 349968 | 1427 | 268581 | 1496 | 282364 | 1981 | 289149 | 1684 | 287093 | 1967 | 275375 | 2826 | 276303 | 2548 | 351953 | 1461 | 1.3 | 0.158454 | 6.2 | 0.345387 | 9.6 | 0.367173 | 10.9 | 0.421216 | 6.5 | 0.322644 | 1.9 | 0.175725 | 0.6 | 0.088902 | 5.2 | 0.124610 | 51 | Virginia | 47 | {'rings': [[[-8458295.369, 4552089.1276], [-84... | 01779803 | VA | 1.673934e+11 | 3.720225e+06 | 1159.14 | 59.9697 |
| 20 | SC | 1036 | 77862731483 | 5071381300 | 1148289 | 1334 | 61725 | 1553 | 2488 | 355 | 14262 | 843 | 18116 | 777 | 15574 | 923 | 7993 | 718 | 2375 | 329 | 917 | 217 | 1086564 | 2135 | 154271 | 955 | 150761 | 1107 | 148504 | 927 | 140833 | 1038 | 144940 | 1977 | 150681 | 2119 | 196574 | 1223 | 156759 | 1019 | 165023 | 1391 | 166620 | 1210 | 156407 | 1389 | 152933 | 2103 | 153056 | 2144 | 197491 | 1242 | 1.6 | 0.226227 | 8.6 | 0.505617 | 10.9 | 0.459598 | 10.0 | 0.583464 | 5.2 | 0.463953 | 1.6 | 0.213852 | 0.5 | 0.109840 | 5.4 | 0.135049 | 45 | South Carolina | 41 | {'rings': [[[-8743152.9102, 4008829.0132], [-8... | 01779799 | SC | 1.174042e+11 | 1.913047e+06 | 1108.39 | 59.5801 |
| 46 | MT | 204 | 376960880222 | 3871059371 | 222821 | 664 | 12139 | 662 | 446 | 128 | 2584 | 368 | 3740 | 390 | 3240 | 385 | 1548 | 221 | 436 | 125 | 145 | 68 | 210682 | 1068 | 30452 | 541 | 32190 | 552 | 27591 | 537 | 28702 | 523 | 29153 | 756 | 26963 | 792 | 35631 | 515 | 30898 | 556 | 34774 | 663 | 31331 | 664 | 31942 | 649 | 30701 | 788 | 27399 | 802 | 35776 | 519 | 1.4 | 0.413451 | 7.4 | 1.048736 | 11.9 | 1.218795 | 10.1 | 1.187559 | 5.0 | 0.708117 | 1.6 | 0.453837 | 0.4 | 0.189981 | 5.4 | 0.296658 | 30 | Montana | 27 | {'rings': [[[-11582648.3446, 6274793.6263], [-... | 00767982 | MT | 8.201726e+11 | 4.615725e+06 | 1092.26 | 59.5049 |
| 15 | MO | 1302 | 178052037005 | 2488190402 | 1407252 | 1622 | 76296 | 1712 | 3489 | 389 | 16646 | 972 | 23356 | 861 | 20049 | 911 | 8871 | 645 | 2828 | 361 | 1057 | 261 | 1330956 | 2415 | 190626 | 918 | 191885 | 1286 | 179620 | 1021 | 179655 | 1020 | 176549 | 2090 | 178696 | 2054 | 233925 | 1282 | 194115 | 997 | 208531 | 1612 | 202976 | 1336 | 199704 | 1368 | 185420 | 2187 | 181524 | 2085 | 234982 | 1308 | 1.8 | 0.200184 | 8.0 | 0.462015 | 11.5 | 0.417372 | 10.0 | 0.450962 | 4.8 | 0.343251 | 1.6 | 0.198065 | 0.4 | 0.111044 | 5.4 | 0.121477 | 29 | Missouri | 26 | {'rings': [[[-10176764.2505, 4921064.8725], [-... | 01779791 | MO | 2.943633e+11 | 3.050106e+06 | 1080.84 | 58.5991 |
| 19 | CO | 1219 | 268425964573 | 1178495763 | 1314134 | 1296 | 69064 | 1764 | 2404 | 332 | 12043 | 820 | 17412 | 919 | 21424 | 1119 | 11618 | 808 | 3189 | 355 | 974 | 206 | 1245070 | 2249 | 166276 | 836 | 166510 | 986 | 181129 | 1036 | 177232 | 1246 | 173695 | 2203 | 169594 | 1954 | 210634 | 1163 | 168680 | 900 | 178553 | 1282 | 198541 | 1385 | 198656 | 1675 | 185313 | 2347 | 172783 | 1986 | 211608 | 1181 | 1.4 | 0.196675 | 6.7 | 0.456687 | 8.8 | 0.458816 | 10.8 | 0.555897 | 6.3 | 0.428728 | 1.8 | 0.204362 | 0.5 | 0.097316 | 5.3 | 0.134047 | 08 | Colorado | 6 | {'rings': [[[-11583155.812, 5012550.0328], [-1... | 01779779 | CO | 4.473109e+11 | 2.708282e+06 | 1078.04 | 56.6563 |
| 27 | OR | 829 | 248604328809 | 6195045325 | 938000 | 1156 | 46891 | 1423 | 1699 | 316 | 8315 | 710 | 13570 | 701 | 13080 | 764 | 6930 | 560 | 2496 | 369 | 801 | 240 | 891109 | 1807 | 117922 | 763 | 120981 | 968 | 123026 | 871 | 126495 | 855 | 128059 | 1931 | 123396 | 1959 | 151230 | 1153 | 119621 | 826 | 129296 | 1200 | 136596 | 1118 | 139575 | 1147 | 134989 | 2011 | 125892 | 1993 | 152031 | 1178 | 1.4 | 0.263986 | 6.4 | 0.545874 | 9.9 | 0.506710 | 9.4 | 0.541931 | 5.1 | 0.407738 | 2.0 | 0.291423 | 0.5 | 0.157810 | 5.0 | 0.151516 | 41 | Oregon | 38 | {'rings': [[[-13739902.4519, 5817179.7538], [-... | 01155107 | OR | 4.851724e+11 | 3.245438e+06 | 1131.48 | 56.5633 |
| 10 | NC | 2192 | 125919712692 | 13470113896 | 2418330 | 2156 | 121971 | 2930 | 5235 | 508 | 25688 | 1407 | 35174 | 1474 | 31407 | 1317 | 16816 | 958 | 5491 | 572 | 2160 | 397 | 2296359 | 3841 | 319881 | 1312 | 312107 | 1528 | 304942 | 1555 | 296291 | 1484 | 306747 | 3124 | 336614 | 3168 | 419777 | 2197 | 325116 | 1407 | 337795 | 2077 | 340116 | 2143 | 327698 | 1984 | 323563 | 3268 | 342105 | 3219 | 421937 | 2233 | 1.6 | 0.156096 | 7.6 | 0.413892 | 10.3 | 0.428455 | 9.6 | 0.397683 | 5.2 | 0.291388 | 1.6 | 0.166517 | 0.5 | 0.094051 | 5.0 | 0.121004 | 37 | North Carolina | 34 | {'rings': [[[-8404865.2607, 4207455.9158], [-8... | 01027616 | NC | 1.958019e+11 | 4.403645e+06 | 1103.25 | 55.6437 |
| 37 | HI | 358 | 16634117742 | 11777681513 | 321374 | 647 | 19748 | 968 | 520 | 152 | 3061 | 386 | 5958 | 525 | 5351 | 462 | 3364 | 407 | 1019 | 212 | 475 | 139 | 301626 | 1224 | 37171 | 191 | 40913 | 397 | 43264 | 528 | 43662 | 490 | 40864 | 945 | 41945 | 915 | 53807 | 691 | 37691 | 244 | 43974 | 554 | 49222 | 745 | 49013 | 673 | 44228 | 1029 | 42964 | 939 | 54282 | 705 | 1.4 | 0.403180 | 7.0 | 0.873400 | 12.1 | 1.050744 | 10.9 | 0.930610 | 7.6 | 0.903056 | 2.4 | 0.490706 | 0.9 | 0.255818 | 6.1 | 0.300637 | 15 | Hawaii | 12 | {'rings': [[[-17321095.4151, 2287790.5412], [-... | 01779782 | HI | 1.909508e+10 | 1.616132e+06 | 897.693 | 55.162 |
| 5 | IL | 2917 | 143784114293 | 6211277447 | 3091828 | 1949 | 156704 | 2888 | 5474 | 474 | 26960 | 1227 | 39116 | 1577 | 48078 | 1371 | 26498 | 1004 | 8103 | 604 | 2475 | 308 | 2935124 | 3924 | 412694 | 870 | 409396 | 1285 | 402723 | 1548 | 393800 | 1493 | 394748 | 2875 | 403151 | 2957 | 518612 | 1857 | 418168 | 991 | 436356 | 1777 | 441839 | 2210 | 441878 | 2027 | 421246 | 3045 | 411254 | 3018 | 521087 | 1882 | 1.3 | 0.113309 | 6.2 | 0.280064 | 8.9 | 0.354160 | 10.9 | 0.306226 | 6.3 | 0.233963 | 2.0 | 0.146154 | 0.5 | 0.059082 | 5.1 | 0.093287 | 17 | Illinois | 14 | {'rings': [[[-9773905.0437, 5234955.0855], [-9... | 01779784 | IL | 2.496541e+11 | 2.747059e+06 | 1059.93 | 53.7209 |
| 42 | NH | 286 | 23187445452 | 1028643155 | 301067 | 670 | 15210 | 832 | 371 | 128 | 2319 | 360 | 3922 | 424 | 5066 | 439 | 2536 | 350 | 690 | 176 | 306 | 118 | 285857 | 1081 | 42986 | 354 | 41316 | 566 | 34004 | 474 | 33273 | 475 | 35445 | 1015 | 40155 | 1045 | 58678 | 634 | 43357 | 376 | 43635 | 671 | 37926 | 636 | 38339 | 647 | 37981 | 1074 | 40845 | 1060 | 58984 | 645 | 0.9 | 0.295130 | 5.3 | 0.820968 | 10.3 | 1.104435 | 13.2 | 1.123125 | 6.7 | 0.901964 | 1.7 | 0.428661 | 0.5 | 0.199974 | 5.1 | 0.275998 | 33 | New Hampshire | 30 | {'rings': [[[-7860509.7182, 5307853.9016], [-7... | 01779794 | NH | 4.599331e+10 | 1.325759e+06 | 1052.68 | 53.1818 |
| 3 | FL | 4170 | 138911437206 | 31398800291 | 4568521 | 3282 | 220509 | 4001 | 7831 | 683 | 40098 | 1713 | 62132 | 1887 | 59055 | 1976 | 35502 | 1738 | 11662 | 912 | 4229 | 457 | 4348012 | 4727 | 571732 | 1277 | 592706 | 1972 | 595319 | 2008 | 570329 | 2166 | 576543 | 4501 | 619527 | 4380 | 821856 | 3083 | 579563 | 1448 | 632804 | 2612 | 657451 | 2756 | 629384 | 2932 | 612045 | 4825 | 631189 | 4474 | 826085 | 3117 | 1.4 | 0.117799 | 6.3 | 0.269433 | 9.5 | 0.284270 | 9.4 | 0.310900 | 5.8 | 0.280260 | 1.8 | 0.143894 | 0.5 | 0.055287 | 4.8 | 0.087476 | 12 | Florida | 10 | {'rings': [[[-9139900.2618, 2825608.9535], [-9... | 00294478 | FL | 1.961979e+11 | 4.679537e+06 | 1095.57 | 52.8799 |
| 29 | PR | 582 | 8868734851 | 4922543816 | 839230 | 1137 | 29668 | 1232 | 3046 | 414 | 8396 | 674 | 7864 | 687 | 5391 | 497 | 3136 | 448 | 1181 | 313 | 654 | 197 | 809562 | 1782 | 115056 | 569 | 114751 | 714 | 105926 | 702 | 104069 | 513 | 112201 | 1891 | 113799 | 1829 | 143760 | 1054 | 118102 | 704 | 123147 | 982 | 113790 | 982 | 109460 | 714 | 115337 | 1943 | 114980 | 1856 | 144414 | 1072 | 2.6 | 0.350207 | 6.8 | 0.544606 | 6.9 | 0.600791 | 4.9 | 0.452909 | 2.7 | 0.385717 | 1.0 | 0.271716 | 0.5 | 0.136372 | 3.5 | 0.146709 | 72 | Puerto Rico | 52 | {'rings': [[[-7404454.6019, 2024993.4011], [-7... | 01779808 | PR | 9.987440e+09 | 7.641856e+05 | 1441.98 | 50.9759 |
| 13 | MD | 1526 | 25150696145 | 6980371026 | 1461505 | 1286 | 74140 | 1749 | 2626 | 388 | 10628 | 778 | 18673 | 921 | 22167 | 1156 | 14005 | 719 | 4697 | 430 | 1344 | 229 | 1387365 | 2022 | 187665 | 662 | 182947 | 1041 | 190061 | 959 | 186703 | 1158 | 185019 | 2183 | 191122 | 2148 | 263848 | 1281 | 190291 | 767 | 193575 | 1300 | 208734 | 1330 | 208870 | 1636 | 199024 | 2298 | 195819 | 2191 | 265192 | 1301 | 1.4 | 0.203822 | 5.5 | 0.400216 | 8.9 | 0.437534 | 10.6 | 0.547176 | 7.0 | 0.352008 | 2.4 | 0.217944 | 0.5 | 0.086317 | 5.1 | 0.119517 | 24 | Maryland | 21 | {'rings': [[[-8486633.6279, 4563782.6974], [-8... | 01714934 | MD | 4.490638e+10 | 3.051474e+06 | 957.736 | 48.5845 |
| 6 | MI | 2553 | 146488062160 | 103998746281 | 2284424 | 1456 | 120630 | 2366 | 4897 | 459 | 25228 | 1075 | 33718 | 1245 | 34445 | 1101 | 16210 | 790 | 4350 | 407 | 1782 | 274 | 2163794 | 2684 | 324623 | 800 | 328378 | 1250 | 275070 | 1257 | 261282 | 1223 | 274742 | 2389 | 297925 | 2392 | 401774 | 1477 | 329520 | 922 | 353606 | 1649 | 308788 | 1769 | 295727 | 1646 | 290952 | 2516 | 302275 | 2426 | 403556 | 1502 | 1.5 | 0.139231 | 7.1 | 0.302185 | 10.9 | 0.398307 | 11.6 | 0.366615 | 5.6 | 0.267214 | 1.4 | 0.134149 | 0.4 | 0.067877 | 5.3 | 0.103426 | 26 | Michigan | 23 | {'rings': [[[-9330373.0182, 5414017.1419], [-9... | 01779789 | MI | 2.963523e+11 | 6.631921e+06 | 894.8 | 47.2503 |
| 2 | NY | 5054 | 122048992746 | 19247151848 | 4867199 | 2378 | 228874 | 3785 | 6271 | 461 | 32318 | 1215 | 56547 | 1762 | 68770 | 2022 | 46074 | 1522 | 13786 | 799 | 5108 | 513 | 4638325 | 4460 | 610357 | 1084 | 665489 | 1475 | 693439 | 1804 | 629101 | 2030 | 591145 | 4543 | 614633 | 4202 | 834161 | 2274 | 616628 | 1178 | 697807 | 1911 | 749986 | 2522 | 697871 | 2865 | 637219 | 4791 | 628419 | 4277 | 839269 | 2331 | 1.0 | 0.074736 | 4.6 | 0.173654 | 7.5 | 0.233566 | 9.9 | 0.286900 | 7.2 | 0.232581 | 2.2 | 0.126265 | 0.6 | 0.061101 | 4.7 | 0.077679 | 36 | New York | 33 | {'rings': [[[-8265089.4336, 4941893.0483], [-8... | 01779796 | NY | 2.373534e+11 | 3.682011e+06 | 963.039 | 45.2857 |
| 12 | MA | 1692 | 20204287539 | 7130763257 | 1668943 | 1288 | 76567 | 2150 | 1512 | 336 | 8448 | 706 | 17081 | 1018 | 25869 | 1095 | 16947 | 1007 | 5468 | 460 | 1242 | 210 | 1592376 | 2362 | 226808 | 630 | 235632 | 897 | 227986 | 1104 | 204024 | 1114 | 191367 | 2226 | 212670 | 2362 | 293889 | 1343 | 228320 | 714 | 244080 | 1142 | 245067 | 1502 | 229893 | 1562 | 208314 | 2443 | 218138 | 2406 | 295131 | 1359 | 0.7 | 0.147147 | 3.5 | 0.288796 | 7.0 | 0.413194 | 11.3 | 0.470132 | 8.1 | 0.473896 | 2.5 | 0.209055 | 0.4 | 0.071128 | 4.6 | 0.128688 | 25 | Massachusetts | 22 | {'rings': [[[-7881849.2341, 5049370.5517], [-7... | 00606926 | MA | 3.892670e+10 | 1.986538e+06 | 986.373 | 45.2524 |
| 8 | NJ | 2241 | 19049723313 | 3542963551 | 2118945 | 1766 | 100779 | 2275 | 1967 | 334 | 12695 | 940 | 21809 | 1094 | 33848 | 1193 | 21584 | 1001 | 6945 | 588 | 1931 | 296 | 2018166 | 2842 | 277098 | 458 | 267988 | 938 | 257929 | 1141 | 255618 | 1210 | 269480 | 2904 | 291613 | 2912 | 398440 | 1603 | 279065 | 567 | 280683 | 1328 | 279738 | 1581 | 289466 | 1699 | 291064 | 3072 | 298558 | 2971 | 400371 | 1630 | 0.7 | 0.119677 | 4.5 | 0.334213 | 7.8 | 0.388590 | 11.7 | 0.406383 | 7.4 | 0.334886 | 2.3 | 0.195582 | 0.5 | 0.073905 | 4.8 | 0.107243 | 34 | New Jersey | 31 | {'rings': [[[-8242603.8795, 4966516.5938], [-8... | 01779795 | NJ | 3.458525e+10 | 1.108955e+06 | 945.535 | 44.9705 |
| 4 | PA | 3283 | 115881477379 | 3397554419 | 2916477 | 2053 | 147285 | 2754 | 5872 | 551 | 25415 | 1141 | 41506 | 1231 | 44084 | 1663 | 21960 | 1040 | 6063 | 528 | 2385 | 300 | 2769192 | 3297 | 402215 | 1008 | 396626 | 1338 | 377163 | 1394 | 350845 | 1662 | 345545 | 3062 | 379587 | 3090 | 517211 | 1850 | 408087 | 1149 | 422041 | 1758 | 418669 | 1860 | 394929 | 2351 | 367505 | 3234 | 385650 | 3135 | 519596 | 1874 | 1.4 | 0.134959 | 6.0 | 0.269187 | 9.9 | 0.290710 | 11.2 | 0.415812 | 6.0 | 0.278061 | 1.6 | 0.136314 | 0.5 | 0.057713 | 5.1 | 0.094309 | 42 | Pennsylvania | 39 | {'rings': [[[-8314999.9816, 5065206.5215], [-8... | 01779798 | PA | 2.054706e+11 | 2.099403e+06 | 888.357 | 44.8629 |
| 44 | DE | 247 | 5047241079 | 1398670234 | 219096 | 542 | 10598 | 810 | 271 | 101 | 1780 | 348 | 3467 | 417 | 2783 | 415 | 1758 | 258 | 452 | 150 | 87 | 50 | 208498 | 1024 | 29386 | 166 | 29250 | 393 | 29204 | 414 | 27262 | 414 | 27342 | 925 | 27127 | 941 | 38927 | 523 | 29657 | 194 | 31030 | 525 | 32671 | 588 | 30045 | 586 | 29100 | 960 | 27579 | 953 | 39014 | 525 | 0.9 | 0.340508 | 5.7 | 1.117288 | 10.6 | 1.261991 | 9.3 | 1.369396 | 6.0 | 0.863907 | 1.6 | 0.540936 | 0.2 | 0.128124 | 4.8 | 0.369268 | 10 | Delaware | 8 | {'rings': [[[-8409364.6405, 4793054.363], [-84... | 01779781 | DE | 8.697981e+09 | 6.108627e+05 | 887.028 | 42.9069 |
| 48 | VT | 141 | 23873457570 | 1031134839 | 140834 | 420 | 6042 | 526 | 170 | 104 | 1034 | 217 | 1695 | 254 | 1701 | 210 | 929 | 161 | 369 | 126 | 144 | 63 | 134792 | 640 | 21061 | 251 | 21190 | 292 | 15775 | 287 | 16527 | 294 | 17180 | 557 | 17373 | 548 | 25686 | 343 | 21231 | 272 | 22224 | 364 | 17470 | 383 | 18228 | 361 | 18109 | 580 | 17742 | 562 | 25830 | 349 | 0.8 | 0.489742 | 4.7 | 0.973444 | 9.7 | 1.438277 | 9.3 | 1.137153 | 5.1 | 0.873746 | 2.1 | 0.707117 | 0.6 | 0.243786 | 4.3 | 0.373145 | 50 | Vermont | 46 | {'rings': [[[-7959464.1401, 5623625.6427], [-7... | 01779802 | VT | 4.827226e+10 | 1.281178e+06 | 998.823 | 42.8511 |
| 41 | RI | 291 | 2677997539 | 1323452846 | 254311 | 659 | 11841 | 796 | 474 | 177 | 1898 | 378 | 2721 | 370 | 4027 | 415 | 2031 | 333 | 519 | 169 | 171 | 91 | 242470 | 1046 | 36493 | 299 | 38155 | 456 | 33280 | 431 | 29329 | 441 | 29291 | 758 | 31202 | 775 | 44720 | 658 | 36967 | 347 | 40053 | 592 | 36001 | 568 | 33356 | 606 | 31322 | 828 | 31721 | 793 | 44891 | 664 | 1.3 | 0.478654 | 4.7 | 0.941147 | 7.6 | 1.020808 | 12.1 | 1.224668 | 6.5 | 1.049241 | 1.6 | 0.531198 | 0.4 | 0.202635 | 4.7 | 0.312663 | 44 | Rhode Island | 40 | {'rings': [[[-7965127.0492, 5034747.1014], [-7... | 01219835 | RI | 5.138826e+09 | 6.848588e+05 | 873.921 | 40.6907 |
| 21 | CT | 962 | 12542619303 | 1815495323 | 840156 | 946 | 36794 | 1315 | 1164 | 251 | 4373 | 468 | 8258 | 502 | 12843 | 798 | 6987 | 541 | 2259 | 345 | 910 | 205 | 803362 | 1651 | 120875 | 453 | 113653 | 612 | 98992 | 561 | 97378 | 814 | 99266 | 1609 | 112580 | 1639 | 160618 | 892 | 122039 | 518 | 118026 | 770 | 107250 | 753 | 110221 | 1140 | 106253 | 1698 | 114839 | 1675 | 161528 | 915 | 1.0 | 0.205632 | 3.7 | 0.395785 | 7.7 | 0.464933 | 11.7 | 0.713899 | 6.6 | 0.498200 | 2.0 | 0.299047 | 0.6 | 0.126873 | 4.4 | 0.156368 | 09 | Connecticut | 7 | {'rings': [[[-8169262.7439, 5021086.1714], [-8... | 01779780 | CT | 2.314619e+10 | 7.820726e+05 | 873.343 | 38.2474 |
| 40 | DC | 307 | 158351639 | 18675956 | 203284 | 406 | 9567 | 728 | 328 | 149 | 1339 | 255 | 1610 | 332 | 3142 | 403 | 2452 | 295 | 585 | 163 | 111 | 76 | 193717 | 772 | 19216 | 241 | 28081 | 340 | 42657 | 308 | 35885 | 417 | 24635 | 785 | 20408 | 766 | 22835 | 415 | 19544 | 283 | 29420 | 425 | 44267 | 453 | 39027 | 580 | 27087 | 839 | 20993 | 783 | 22946 | 422 | 1.7 | 0.761995 | 4.6 | 0.864260 | 3.6 | 0.749070 | 8.1 | 1.025663 | 9.1 | 1.052371 | 2.8 | 0.769461 | 0.5 | 0.331093 | 4.7 | 0.357723 | 11 | District of Columbia | 9 | {'rings': [[[-8575943.174, 4691871.4072], [-85... | 01702382 | DC | 2.927454e+08 | 8.630067e+04 | 662.163 | 31.1629 |
# Plot No. of Mothers (age 15 to 50) per OB-GYN Provider by State
plt.figure(figsize=(25,12))
sns.barplot(state_obgyn_df['regionabbr'], state_obgyn_df['mother_per_prov'])
plt.title('No. of Mothers (age 15 to 50) per OB-GYN Provider by State', fontsize=22)
plt.xlabel('States', fontsize=18)
plt.ylabel('No. of Mothers (age 15 to 50)', fontsize=18)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
(array([ 0., 20., 40., 60., 80., 100., 120.]), <a list of 7 Text yticklabel objects>)
On average, there were ~112 mothers and 1655 women per provider in ND compared to ~31 mothers and 662 women per provider in DC. The difference is drastic.
North Dakota has the highest number of mothers and women per OB-GYN provider. From the fertility map above, we also saw that percent of women (15 to 50) who had a birth in the past 12 months is high in North Dakota.
Let's explore North Dakota to find out which counties have the lowest number of OBGYN providers.
# State population df
ND_featureset = fertility_county.query(where="STATE='North Dakota'")
ND_fertility_df = ND_featureset.sdf
ND_fertility_df.head()
| ALAND | AWATER | B13016_001E | B13016_001M | B13016_002E | B13016_002M | B13016_003E | B13016_003M | B13016_004E | B13016_004M | B13016_005E | B13016_005M | B13016_006E | B13016_006M | B13016_007E | B13016_007M | B13016_008E | B13016_008M | B13016_009E | B13016_009M | B13016_010E | B13016_010M | B13016_011E | B13016_011M | B13016_012E | B13016_012M | B13016_013E | B13016_013M | B13016_014E | B13016_014M | B13016_015E | B13016_015M | B13016_016E | B13016_016M | B13016_017E | B13016_017M | B13016_calc_num15to19E | B13016_calc_num15to19M | B13016_calc_num20to24E | B13016_calc_num20to24M | B13016_calc_num25to29E | B13016_calc_num25to29M | B13016_calc_num30to34E | B13016_calc_num30to34M | B13016_calc_num35to39E | B13016_calc_num35to39M | B13016_calc_num40to44E | B13016_calc_num40to44M | B13016_calc_num45to50E | B13016_calc_num45to50M | B13016_calc_pct15to19E | B13016_calc_pct15to19M | B13016_calc_pct20to24E | B13016_calc_pct20to24M | B13016_calc_pct25to29E | B13016_calc_pct25to29M | B13016_calc_pct30to34E | B13016_calc_pct30to34M | B13016_calc_pct35to39E | B13016_calc_pct35to39M | B13016_calc_pct40to44E | B13016_calc_pct40to44M | B13016_calc_pct45to50E | B13016_calc_pct45to50M | B13016_calc_pctBirthsE | B13016_calc_pctBirthsM | COUNTYNS | GEOID | NAME | OBJECTID | SHAPE | Shape__Area | Shape__Length | State | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2557960600 | 2833854 | 446 | 58 | 19 | 20 | 12 | 17 | 0 | 9 | 0 | 9 | 0 | 9 | 7 | 11 | 0 | 9 | 0 | 9 | 427 | 65 | 37 | 24 | 67 | 29 | 53 | 24 | 56 | 21 | 48 | 23 | 103 | 37 | 63 | 31 | 49 | 29 | 67 | 30 | 53 | 26 | 56 | 23 | 55 | 25 | 103 | 38 | 63 | 32 | 24.5 | 31.521266 | 0.0 | 13.432836 | 0.0 | 16.981132 | 0.0 | 16.071429 | 12.7 | 19.145034 | 0.0 | 8.737864 | 0.0 | 14.285714 | 4.3 | 4.480234 | 01034210 | 38001 | Adams County | 1991 | {"rings": [[[-11354354.1023, 5813340.1041], [-... | 5.324157e+09 | 330639.744151 | North Dakota |
| 1 | 3863107435 | 56591279 | 2234 | 110 | 173 | 73 | 0 | 15 | 5 | 10 | 124 | 63 | 18 | 22 | 26 | 35 | 0 | 15 | 0 | 15 | 2061 | 127 | 391 | 64 | 317 | 79 | 216 | 70 | 267 | 62 | 238 | 73 | 274 | 53 | 358 | 48 | 391 | 66 | 322 | 80 | 340 | 94 | 285 | 66 | 264 | 81 | 274 | 55 | 358 | 50 | 0.0 | 3.836317 | 1.6 | 3.081535 | 36.5 | 15.545781 | 6.3 | 7.579469 | 9.8 | 12.908628 | 0.0 | 5.474453 | 0.0 | 4.189944 | 7.7 | 3.257869 | 01034225 | 38003 | Barnes County | 1992 | {"rings": [[[-10904992.8409, 5981421.7966], [-... | 8.403601e+09 | 374027.679170 | North Dakota |
| 2 | 3596568808 | 131708144 | 1390 | 16 | 128 | 33 | 8 | 9 | 68 | 24 | 20 | 13 | 18 | 11 | 7 | 6 | 7 | 7 | 0 | 13 | 1262 | 37 | 202 | 21 | 181 | 25 | 178 | 13 | 166 | 12 | 155 | 24 | 140 | 25 | 240 | 16 | 210 | 23 | 249 | 35 | 198 | 18 | 184 | 16 | 162 | 25 | 147 | 26 | 240 | 21 | 3.8 | 4.265356 | 27.3 | 8.841183 | 10.1 | 6.501124 | 9.8 | 5.917430 | 4.3 | 3.643182 | 4.8 | 4.686829 | 0.0 | 5.416667 | 9.2 | 2.364013 | 01034216 | 38005 | Benson County | 1993 | {"rings": [[[-11075502.1995, 6168791.7355], [-... | 8.343651e+09 | 537806.413896 | North Dakota |
| 3 | 2975481852 | 11990484 | 174 | 36 | 5 | 5 | 0 | 9 | 0 | 9 | 3 | 4 | 0 | 9 | 2 | 3 | 0 | 9 | 0 | 9 | 169 | 35 | 20 | 19 | 12 | 11 | 10 | 10 | 33 | 15 | 32 | 13 | 14 | 11 | 48 | 16 | 20 | 21 | 12 | 14 | 13 | 11 | 33 | 17 | 34 | 13 | 14 | 14 | 48 | 18 | 0.0 | 45.000000 | 0.0 | 75.000000 | 23.1 | 23.779327 | 0.0 | 27.272727 | 5.9 | 8.532061 | 0.0 | 64.285714 | 0.0 | 18.750000 | 2.9 | 2.872377 | 01035616 | 38007 | Billings County | 1994 | {"rings": [[[-11476951.3137, 5995895.8254], [-... | 6.425825e+09 | 369444.155012 | North Dakota |
| 4 | 4321196493 | 74864998 | 1204 | 49 | 68 | 43 | 0 | 13 | 11 | 14 | 16 | 14 | 24 | 29 | 17 | 21 | 0 | 13 | 0 | 13 | 1136 | 67 | 163 | 37 | 159 | 43 | 117 | 15 | 141 | 27 | 175 | 49 | 132 | 37 | 249 | 42 | 163 | 39 | 170 | 45 | 133 | 21 | 165 | 40 | 192 | 53 | 132 | 39 | 249 | 44 | 0.0 | 7.975460 | 6.5 | 8.055208 | 12.0 | 10.353515 | 14.5 | 17.218402 | 8.9 | 10.660919 | 0.0 | 9.848485 | 0.0 | 5.220884 | 5.6 | 3.565728 | 01034227 | 38009 | Bottineau County | 1995 | {"rings": [[[-11152289.0833, 6274727.6819], [-... | 1.011984e+10 | 483713.596470 | North Dakota |
# Get provider data for obgyn providers only
obgyn_ND_featureset = provider_data_layer.query(where="Region='North Dakota' AND (user_taxonomy_code_1 in ('207V00000X','207VC0200X','207VF0040X','207VX0201X','207VG0400X','207VH0002X','207VM0101X','207VB0002X','207VX0000X','207VE0102X','363LX0001X'))")
# out_fields='user_npi,user_entity_type,user_provider_gender,user_taxonomy_code_1,user_full_address,Postal,City,Subregion,Region,RegionAbbr')
obgyn_ND_df = obgyn_ND_featureset.sdf
obgyn_ND_df.head()
| SHAPE | addnumfrom | addnumto | addr_type | addressnumber | addressrange | bldgname | bldgtype | block | buildingname | city | country | displayx | displayy | distance | district | extrainfo | in_address | in_address2 | in_city | in_postal | in_region | langcode | levelname | leveltype | loc_name | longlabel | match_addr | metroarea | neighborhood | objectid | phone | place_addr | placename | postal | postalext | rank | region | regionabbr | score | sector | shortlabel | side | staddr | status | stdir | stname | stpredir | stpretype | sttype | subaddress | subregion | territory | type | unitname | unittype | url | user_address | user_address2 | user_city | user_country | user_entity_type | user_full_address | user_npi | user_organization_name | user_postal | user_provider_gender | user_region | user_taxonomy_code_1 | user_taxonomy_code_10 | user_taxonomy_code_11 | user_taxonomy_code_12 | user_taxonomy_code_13 | user_taxonomy_code_14 | user_taxonomy_code_15 | user_taxonomy_code_2 | user_taxonomy_code_3 | user_taxonomy_code_4 | user_taxonomy_code_5 | user_taxonomy_code_6 | user_taxonomy_code_7 | user_taxonomy_code_8 | user_taxonomy_code_9 | user_taxonomy_group_1 | x | x_max | x_min | y | y_max | y_min | zone | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | {"x": -96.78767519099995, "y": 46.884720309000... | 801 | 825 | StreetAddress | 801 | 801-825 | Fargo | USA | -96.787675 | 46.884720 | 0.0 | 801 BROADWAY N | None | FARGO | 58122.0 | ND | ENG | World | 801 Broadway N, Fargo, ND, 58102, USA | 801 Broadway N, Fargo, North Dakota, 58102 | Downtown Fargo | 3720 | 801 Broadway N, Fargo, North Dakota, 58102 | 58102 | 20.0 | North Dakota | ND | 99.51 | 801 Broadway N | R | 801 Broadway N | M | N | Broadway | Cass County | 801 BROADWAY N | None | FARGO | US | Individual | 801 BROADWAY N, , FARGO, ND 58122 | 1.851398e+09 | None | 58122.0 | M | ND | 207VM0101X | None | None | None | None | None | None | 207VM0101X | None | None | None | None | None | None | None | None | -96.787675 | -96.786675 | -96.788675 | 46.884720 | 46.885720 | 46.883720 | |||||||||||||||||||||||
| 1 | {"x": -97.06651079899996, "y": 47.912072701000... | 1098 | 1000 | StreetAddress | 1000 | 1000-1098 | Grand Forks | USA | -97.066511 | 47.912073 | 0.0 | 582066002 | 1000 SOUTH COLUMBIA ROAD | None | GRAND FORKS | 582066002.0 | ND | ENG | World | 1000 S Columbia Rd, Grand Forks, ND, 58201, USA | 1000 S Columbia Rd, Grand Forks, North Dakota,... | 12480 | 1000 S Columbia Rd, Grand Forks, North Dakota,... | 58201 | 20.0 | North Dakota | ND | 97.86 | 1000 S Columbia Rd | L | 1000 S Columbia Rd | M | Columbia | S | Rd | Grand Forks County | 1000 SOUTH COLUMBIA ROAD | None | GRAND FORKS | US | Individual | 1000 SOUTH COLUMBIA ROAD, , GRAND FORKS, ND 58... | 1.366440e+09 | None | 582066002.0 | M | ND | 207V00000X | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | -97.066511 | -97.065511 | -97.067511 | 47.912073 | 47.913073 | 47.911073 | ||||||||||||||||||||||
| 2 | {"x": -99.74991768599995, "y": 48.838085857000... | 1 | 1099 | StreetAddress | 1 | 1-1099 | Belcourt | USA | -99.749918 | 48.838086 | 0.0 | 1 HOSPITAL ROAD NORTH | None | BELCOURT | 58316.0 | ND | ENG | World | 1 Hospital Rd, Belcourt, ND, 58316, USA | 1 Hospital Rd, Belcourt, North Dakota, 58316 | 45476 | 1 Hospital Rd, Belcourt, North Dakota, 58316 | 58316 | 20.0 | North Dakota | ND | 98.87 | 1 Hospital Rd | L | 1 Hospital Rd | M | Hospital | Rd | Rolette County | 1 HOSPITAL ROAD NORTH | None | BELCOURT | US | Individual | 1 HOSPITAL ROAD NORTH, , BELCOURT, ND 58316 | 1.164496e+09 | None | 58316.0 | M | ND | 207V00000X | None | None | None | None | None | None | 207V00000X | None | None | None | None | None | None | None | None | -99.749918 | -99.748918 | -99.750918 | 48.838086 | 48.839086 | 48.837086 | ||||||||||||||||||||||||
| 3 | {"x": -100.77651963899996, "y": 46.80847046400... | PointAddress | 1000 | Bismarck | USA | -100.776510 | 46.808793 | 0.0 | 1000 E ROSSER AVE | None | BISMARCK | 585014414.0 | ND | ENG | World | 1000 E Rosser Ave, Bismarck, ND, 58501, USA | 1000 E Rosser Ave, Bismarck, North Dakota, 58501 | Downtown Bismarck | 60149 | 1000 E Rosser Ave, Bismarck, North Dakota, 58501 | 58501 | 20.0 | North Dakota | ND | 100.00 | 1000 E Rosser Ave | R | 1000 E Rosser Ave | M | Rosser | E | Ave | Burleigh County | 1000 E ROSSER AVE | None | BISMARCK | US | Individual | 1000 E ROSSER AVE, , BISMARCK, ND 585014414 | 1.982658e+09 | None | 585014414.0 | M | ND | 207V00000X | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | -100.776520 | -100.775510 | -100.777510 | 46.808470 | 46.809793 | 46.807793 | |||||||||||||||||||||||||
| 4 | {"x": -100.77651963899996, "y": 46.80847046400... | PointAddress | 1000 | Bismarck | USA | -100.776510 | 46.808793 | 0.0 | 1000 E ROSSER AVE | None | BISMARCK | 585014414.0 | ND | ENG | World | 1000 E Rosser Ave, Bismarck, ND, 58501, USA | 1000 E Rosser Ave, Bismarck, North Dakota, 58501 | Downtown Bismarck | 60879 | 1000 E Rosser Ave, Bismarck, North Dakota, 58501 | 58501 | 20.0 | North Dakota | ND | 100.00 | 1000 E Rosser Ave | R | 1000 E Rosser Ave | M | Rosser | E | Ave | Burleigh County | 1000 E ROSSER AVE | None | BISMARCK | US | Individual | 1000 E ROSSER AVE, , BISMARCK, ND 585014414 | 1.093769e+09 | None | 585014414.0 | M | ND | 207V00000X | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | -100.776520 | -100.775510 | -100.777510 | 46.808470 | 46.809793 | 46.807793 |
# Create dataframe of obgyn provider counts by county
obgyn_NDcounty_df = pd.DataFrame(obgyn_ND_df['subregion'].value_counts().reset_index().values, columns=['County','Provider_Count'])
obgyn_NDcounty_df.head()
| County | Provider_Count | |
|---|---|---|
| 0 | Cass County | 36 |
| 1 | Burleigh County | 22 |
| 2 | Grand Forks County | 13 |
| 3 | Ward County | 10 |
| 4 | Williams County | 8 |
# Merge provider count and women data at county level for ND
county_obgyn_df = pd.merge(ND_fertility_df,obgyn_NDcounty_df,left_on='NAME', right_on='County',how='left')
# Create new columns that shows provider by women pop
county_obgyn_df['women_per_prov'] = county_obgyn_df['B13016_001E']/county_obgyn_df['Provider_Count']
# Create new columns that shows provider by mother pop
county_obgyn_df['mother_per_prov'] = county_obgyn_df['B13016_002E']/county_obgyn_df['Provider_Count']
# Arrange dataframe by mother_per_prov descending and then B13016_002E (women who gave birht) descending
county_obgyn_df = county_obgyn_df.sort_values(by=['mother_per_prov','B13016_002E'], ascending=False)[['NAME','Provider_Count','B13016_001E','B13016_002E','women_per_prov','mother_per_prov']]
county_obgyn_df.columns = ['Name','Provider_Count', "Total Women (15 to 50)","Women who had birth (past 12 months)", 'women_per_prov', 'mother_per_prov']
county_obgyn_df
| Name | Provider_Count | Total Women (15 to 50) | Women who had birth (past 12 months) | women_per_prov | mother_per_prov | |
|---|---|---|---|---|---|---|
| 38 | Richland County | 1 | 3459 | 231 | 3459 | 231 |
| 46 | Stutsman County | 2 | 4275 | 303 | 2137.5 | 151.5 |
| 44 | Stark County | 5 | 6850 | 592 | 1370 | 118.4 |
| 50 | Ward County | 10 | 16362 | 1141 | 1636.2 | 114.1 |
| 52 | Williams County | 8 | 7258 | 779 | 907.25 | 97.375 |
| 8 | Cass County | 36 | 44821 | 2936 | 1245.03 | 81.5556 |
| 42 | Sioux County | 1 | 1056 | 75 | 1056 | 75 |
| 17 | Grand Forks County | 13 | 17889 | 895 | 1376.08 | 68.8462 |
| 39 | Rolette County | 3 | 3262 | 171 | 1087.33 | 57 |
| 7 | Burleigh County | 22 | 20934 | 1213 | 951.545 | 55.1364 |
| 29 | Morton County | NaN | 6575 | 463 | NaN | NaN |
| 1 | Barnes County | NaN | 2234 | 173 | NaN | NaN |
| 26 | McKenzie County | NaN | 2528 | 150 | NaN | NaN |
| 10 | Dickey County | NaN | 1026 | 149 | NaN | NaN |
| 24 | McHenry County | NaN | 1128 | 137 | NaN | NaN |
| 33 | Pembina County | NaN | 1206 | 129 | NaN | NaN |
| 2 | Benson County | NaN | 1390 | 128 | NaN | NaN |
| 49 | Walsh County | NaN | 1982 | 120 | NaN | NaN |
| 35 | Ramsey County | NaN | 2372 | 117 | NaN | NaN |
| 30 | Mountrail County | NaN | 2078 | 113 | NaN | NaN |
| 27 | McLean County | NaN | 1654 | 109 | NaN | NaN |
| 28 | Mercer County | NaN | 1612 | 98 | NaN | NaN |
| 5 | Bowman County | NaN | 660 | 90 | NaN | NaN |
| 51 | Wells County | NaN | 612 | 84 | NaN | NaN |
| 34 | Pierce County | NaN | 773 | 76 | NaN | NaN |
| 4 | Bottineau County | NaN | 1204 | 68 | NaN | NaN |
| 11 | Divide County | NaN | 396 | 65 | NaN | NaN |
| 20 | Hettinger County | NaN | 533 | 64 | NaN | NaN |
| 12 | Dunn County | NaN | 828 | 60 | NaN | NaN |
| 13 | Eddy County | NaN | 420 | 57 | NaN | NaN |
| 48 | Traill County | NaN | 1637 | 57 | NaN | NaN |
| 15 | Foster County | NaN | 631 | 53 | NaN | NaN |
| 36 | Ransom County | NaN | 1057 | 52 | NaN | NaN |
| 19 | Griggs County | NaN | 391 | 50 | NaN | NaN |
| 9 | Cavalier County | NaN | 597 | 35 | NaN | NaN |
| 47 | Towner County | NaN | 341 | 35 | NaN | NaN |
| 16 | Golden Valley County | NaN | 368 | 34 | NaN | NaN |
| 37 | Renville County | NaN | 517 | 34 | NaN | NaN |
| 40 | Sargent County | NaN | 735 | 32 | NaN | NaN |
| 32 | Oliver County | NaN | 306 | 31 | NaN | NaN |
| 21 | Kidder County | NaN | 426 | 30 | NaN | NaN |
| 31 | Nelson County | NaN | 461 | 28 | NaN | NaN |
| 14 | Emmons County | NaN | 570 | 26 | NaN | NaN |
| 6 | Burke County | NaN | 403 | 22 | NaN | NaN |
| 22 | LaMoure County | NaN | 691 | 20 | NaN | NaN |
| 0 | Adams County | NaN | 446 | 19 | NaN | NaN |
| 23 | Logan County | NaN | 275 | 15 | NaN | NaN |
| 18 | Grant County | NaN | 390 | 14 | NaN | NaN |
| 41 | Sheridan County | NaN | 224 | 14 | NaN | NaN |
| 45 | Steele County | NaN | 293 | 10 | NaN | NaN |
| 25 | McIntosh County | NaN | 407 | 7 | NaN | NaN |
| 3 | Billings County | NaN | 174 | 5 | NaN | NaN |
| 43 | Slope County | NaN | 75 | 1 | NaN | NaN |
# Create provider count df
from arcgis.geoanalytics import summarize_data
fertility_state = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/0")
sum_fields = ['user_npi Count']
eq_summary = summarize_data.aggregate_points(point_layer = provider_data_layer.filter("OBJECTID < 10"),
polygon_layer = fertility_state,
output_name='test_women_agg')
# group_by_field = 'STUSPS',
# summary_fields=sum_fields)
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-100-ad333b42341e> in <module> 3 fertility_state = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/0") 4 sum_fields = ['user_npi Count'] ----> 5 eq_summary = summarize_data.aggregate_points(point_layer = provider_data_layer.filter("OBJECTID < 10"), 6 polygon_layer = fertility_state, 7 output_name='test_women_agg') TypeError: 'NoneType' object is not callable
# Create provider count df
from arcgis.features import summarize_data
fertility_state = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/0")
sum_fields = ['user_npi Count']
eq_summary = summarize_data.aggregate_points(point_layer = provider_data_layer,
polygon_layer = fertility_state,
keep_boundaries_with_no_points=False,
group_by_field = 'STUSPS',
summary_fields = sum_fields)
GetLayers for parameter 0 failed. Error: {"code" : 0, "messageCode":"GPEXT_018","message": "Number of features in service https://datascienceqa.esri.com/server/rest/services/Hosted/provider_clean_data_geocoded_6_19/FeatureServer/0 exceeds the limit of 100,000 features. Use a hosted feature service as input to analyze large dataset.","params":{"url" : "https://datascienceqa.esri.com/server/rest/services/Hosted/provider_clean_data_geocoded_6_19/FeatureServer/0"}}
{"messageCode": "AO_100001", "message": "AggregatePoints failed."}
Failed to execute (AggregatePoints).
Failed.
--------------------------------------------------------------------------- Exception Traceback (most recent call last) <ipython-input-11-860b6902dbc3> in <module> 7 keep_boundaries_with_no_points=False, 8 group_by_field = 'STUSPS', ----> 9 summary_fields=sum_fields) ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\features\summarize_data.py in aggregate_points(point_layer, polygon_layer, keep_boundaries_with_no_points, summary_fields, group_by_field, minority_majority, percent_points, output_name, context, gis, estimate) 95 output_name, 96 context, ---> 97 estimate=estimate) 98 99 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\tools.py in aggregate_points(self, point_layer, polygon_layer, keep_boundaries_with_no_points, summary_fields, group_by_field, minority_majority, percent_points, output_name, context, estimate) 504 task_url, job_info, job_id = super()._analysis_job(task, params) 505 --> 506 job_info = super()._analysis_job_status(task_url, job_info) 507 job_values = super()._analysis_job_results(task_url, job_info, job_id) 508 #print(job_values) ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\tools.py in _analysis_job_status(self, task_url, job_info) 187 188 if job_response.get("jobStatus") == "esriJobFailed": --> 189 raise Exception("Job failed.") 190 elif job_response.get("jobStatus") == "esriJobCancelled": 191 raise Exception("Job cancelled.") Exception: Job failed.
import arcpy
# Set local variables
# This example used a big data file share name "Crimes" with dataset "Chicago" registered on my GeoAnalytics server
inFeatures = "https://MyGeoAnalyticsMachine.domain.com/geoanalytics/rest/services/DataStoreCatalogs/bigDataFileShares_Crimes/BigDataCatalogServer/Chicago"
summaryFields = "Region"
# summaryStatistics = [["user_npi", "COUNT"]]
outFS = 'SummarizeStates'
# Execute SummarizeAttributes
arcpy.geoanalytics.SummarizeAttributes(provider_data_layer, outFS, summaryFields)
--------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-9-85d7a01ac9d5> in <module> 10 11 # Execute SummarizeAttributes ---> 12 arcpy.geoanalytics.SummarizeAttributes(provider_data_layer, outFS, summaryFields) ~\AppData\Local\Programs\ArcGIS\Pro\Resources\ArcPy\arcpy\geoanalytics.py in SummarizeAttributes(input_layer, output_name, fields, summary_fields, data_store) 1732 return retval 1733 except Exception as e: -> 1734 raise e 1735 1736 @gptooldoc('SummarizeWithin_geoanalytics', None) ~\AppData\Local\Programs\ArcGIS\Pro\Resources\ArcPy\arcpy\geoanalytics.py in SummarizeAttributes(input_layer, output_name, fields, summary_fields, data_store) 1729 from arcpy.arcobjects.arcobjectconversion import convertArcObjectToPythonObject 1730 try: -> 1731 retval = convertArcObjectToPythonObject(gp.SummarizeAttributes_geoanalytics(*gp_fixargs((input_layer, output_name, fields, summary_fields, data_store), True))) 1732 return retval 1733 except Exception as e: ~\AppData\Local\Programs\ArcGIS\Pro\Resources\ArcPy\arcpy\geoprocessing\_base.py in <lambda>(*args) 504 val = getattr(self._gp, attr) 505 if callable(val): --> 506 return lambda *args: val(*gp_fixargs(args, True)) 507 else: 508 return convertArcObjectToPythonObject(val) RuntimeError: Object: Error in executing tool
The idea of clustering is to find out if observations in the data naturally group together in some predictable way. In our case, we want to know if there is natural grouping that distinguishes some states from others. Standardization - most cluster analysis algorithms depend on the concept of measuring the distance between the different observations we're trying to cluster. If one of the variables is measured on a much larger scale than the other variables, then whatever measure we use will be overly influenced by the variable that has larger scale. So, we standardize our data to bring all variables to the same scale.
# Get provider data for obgyn providers only
obgyn_df.head()
| SHAPE | city | objectid | postal | region | regionabbr | subregion | user_entity_type | user_full_address | user_npi | user_provider_gender | user_taxonomy_code_1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | {"x": -97.49116252599998, "y": 35.261905469000... | Norman | 8 | 73072 | Oklahoma | OK | Cleveland County | Individual | 3400 W TECUMSEH RD, SUITE 205, NORMAN, OK 7307... | 1.487658e+09 | F | 207V00000X |
| 1 | {"x": -120.00990533799995, "y": 46.31641132900... | Sunnyside | 24 | 98944 | Washington | WA | Yakima County | Individual | 803 E LINCOLN AVE, , SUNNYSIDE, WA 989442383 | 1.265435e+09 | F | 207V00000X |
| 2 | {"x": -81.63739261199999, "y": 38.359802984000... | Charleston | 26 | 25302 | West Virginia | WV | Kanawha County | Individual | 830 PENNSYLVANIA AVE, SUITE 108, CHARLESTON, W... | 1.447253e+09 | M | 207V00000X |
| 3 | {"x": -81.63739261199999, "y": 38.359802984000... | Charleston | 99 | 25302 | West Virginia | WV | Kanawha County | Individual | 830 PENNSYLVANIA AVE, STE 402, CHARLESTON, WV ... | 1.487658e+09 | M | 207V00000X |
| 4 | {"x": -81.63739261199999, "y": 38.359802984000... | Charleston | 172 | 25302 | West Virginia | WV | Kanawha County | Individual | 830 PENNSYLVANIA AVE, STE 402, CHARLESTON, WV ... | 1.255334e+09 | M | 207V00000X |
obgyn_df.shape
(66754, 12)
obgyn_county_df = obgyn_df[['regionabbr','subregion']]
obgyn_county_df['Provider_Count'] = obgyn_county_df.groupby(['regionabbr','subregion'])['subregion'].transform('count')
obgyn_count_df = obgyn_county_df.drop_duplicates(subset='subregion', keep="first", inplace=True)
obgyn_county_df.shape
C:\Users\mohi9282\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\ipykernel_launcher.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy C:\Users\mohi9282\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\ipykernel_launcher.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy This is separate from the ipykernel package so we can avoid doing imports until
(1282, 3)
obgyn_county_df = obgyn_county_df.sort_values(by=['Provider_Count'], ascending=False)
obgyn_county_df.head()
| regionabbr | subregion | Provider_Count | |
|---|---|---|---|
| 100 | CA | Los Angeles County | 2094 |
| 37 | IL | Cook County | 1586 |
| 1167 | NY | New York County | 1136 |
| 9 | TX | Harris County | 1124 |
| 134 | AZ | Maricopa County | 844 |
# Check missing
obgyn_county_df.isnull().sum()
regionabbr 0 subregion 0 Provider_Count 0 dtype: int64
county_layer = popdensity.layers[46]
county_df = pd.DataFrame()
offset = 0
while offset <= 3000:
county_featureset_test = county_layer.query(return_all_records=False,
result_offset=offset,
result_record_count=750)
county_df_test = county_featureset_test.sdf
county_df = pd.concat([county_df_test, county_df], ignore_index=True)
offset += 750
county_df.shape
(3142, 1943)
# Removing columns for 2023,2010,2000
county_df = county_df.loc[:,~county_df.columns.str.contains('FY|10|00')]
len(county_df.columns)
894
# Removing columns for individual age
county_df = county_df.loc[:,~county_df.columns.str.startswith(('AGE','MAGE','FAGE'))]
len(county_df.columns)
640
# Removing columns for Industry, Occupation
county_df = county_df.loc[:,~county_df.columns.str.startswith(('IND','OCC'))]
len(county_df.columns)
596
# Removing Individual Income Columns
county_df = county_df.loc[:,~county_df.columns.str.contains('A15|A25|A35|A45|A55|A65|A75')]
len(county_df.columns)
366
# Removing columns for Disposable Income and Net Worth
county_df = county_df.loc[:,~county_df.columns.str.startswith(('DI','NW'))]
len(county_df.columns)
348
# Removing columns for Tapestry Segmentation
county_df = county_df.loc[:,~county_df.columns.str.startswith(('TSE','THH','TADULT'))]
len(county_df.columns)
190
# Removing columns for Home Value
county_df = county_df.loc[:,~county_df.columns.str.startswith(('VAL'))]
len(county_df.columns)
181
county_df.columns
Index(['AAGEBASECY', 'AGGDI_CY', 'AGGHINC_CY', 'AGGINC_CY', 'AGGNW_CY',
'AIFBASE_CY', 'AIMBASE_CY', 'AMERIND_CY', 'AREA', 'ASIAN_CY',
...
'TOTHU_CY', 'TOTPOP_CY', 'UNEMPRT_CY', 'UNEMP_CY', 'VACANT_CY',
'WAGEBASECY', 'WHITE_CY', 'WHTFBASECY', 'WHTMBASECY', 'WIDOWED_CY'],
dtype='object', length=181)
print(obgyn_county_df.shape)
print(county_df.shape)
(1282, 3) (3142, 181)
# Merge provider count and women_df at state level
newcounty_obgyn_df = pd.merge(obgyn_county_df,county_df,left_on=['regionabbr','subregion'], right_on=['ST_ABBREV','NAME'],how='left')
newcounty_obgyn_df.shape
(1282, 184)
# Create a copy
test_newcounty_df = newcounty_obgyn_df.copy()
test_newcounty_df.head()
| regionabbr | subregion | Provider_Count | AAGEBASECY | AGGDI_CY | AGGHINC_CY | AGGINC_CY | AGGNW_CY | AIFBASE_CY | AIMBASE_CY | AMERIND_CY | AREA | ASIAN_CY | ASNFBASECY | ASNMBASECY | ASSCDEG_CY | AVGDI_CY | AVGFMSZ_CY | AVGHHSZ_CY | AVGHINC_CY | AVGNW_CY | AVGVAL_CY | BABYBOOMCY | BACHDEG_CY | BAGEBASECY | BLACK_CY | BLKFBASECY | BLKMBASECY | CIVLBFR_CY | EDUCBASECY | EMP_CY | FAMHH_CY | FAMPOP_CY | FEM0_CY | FEM15_CY | FEM18UP_CY | FEM20_CY | FEM21UP_CY | FEM25_CY | FEM30_CY | FEM35_CY | FEM40_CY | FEM45_CY | FEM50_CY | FEM55_CY | FEM5_CY | FEM60_CY | FEM65_CY | FEM70_CY | FEM75_CY | FEM80_CY | FEM85_CY | FEMALES_CY | GED_CY | GENALPHACY | GENBASE_CY | GENX_CY | GENZ_CY | GQPOP_CY | GRADDEG_CY | HAGEBASECY | HHPOP_CY | HINC0_CY | HINC150_CY | HINC15_CY | HINC25_CY | HINC35_CY | HINC50_CY | HINC75_CY | HINCBASECY | HISPAI_CY | HISPASN_CY | HISPBLK_CY | HISPMLT_CY | HISPOTH_CY | HISPPI_CY | HISPPOP_CY | HISPWHT_CY | HSGRAD_CY | HSPFBASECY | HSPMBASECY | IAGEBASECY | ID | LANDAREA | MAL18UP_CY | MAL21UP_CY | MALE0_CY | MALE15_CY | MALE20_CY | MALE25_CY | MALE30_CY | MALE35_CY | MALE40_CY | MALE45_CY | MALE50_CY | MALE55_CY | MALE5_CY | MALE60_CY | MALE65_CY | MALE70_CY | MALE75_CY | MALE80_CY | MALE85_CY | MALES_CY | MARBASE_CY | MARRIED_CY | MEDAGE_CY | MEDDI_CY | MEDFAGE_CY | MEDHHR_CY | MEDHINC_CY | MEDMAGE_CY | MEDNW_CY | MEDVAL_CY | MILLENN_CY | MINORITYCY | MLTFBASECY | MLTMBASECY | NAME | NEVMARR_CY | NHSPAI_CY | NHSPASN_CY | NHSPBLK_CY | NHSPMLT_CY | NHSPOTH_CY | NHSPPI_CY | NHSPWHT_CY | NOHS_CY | NONHISP_CY | OAGEBASECY | OBJECTID | OLDRGENSCY | OTHFBASECY | OTHMBASECY | OTHRACE_CY | OWNER_CY | PACIFIC_CY | PAGEBASECY | PCI_CY | PIFBASE_CY | PIMBASE_CY | POP0_CY | POP15_CY | POP18UP_CY | POP20_CY | POP21UP_CY | POP25_CY | POP30_CY | POP35_CY | POP40_CY | POP45_CY | POP50_CY | POP55_CY | POP5_CY | POP60_CY | POP65_CY | POP70_CY | POP75_CY | POP80_CY | POP85_CY | POPDENS_CY | RACE2UP_CY | RACEBASECY | RENTER_CY | SHAPE | SMCOLL_CY | SOMEHS_CY | STATE_NAME | ST_ABBREV | Shape_Area | Shape_Length | TLIFECODE | TLIFENAME | TOTHH_CY | TOTHU_CY | TOTPOP_CY | UNEMPRT_CY | UNEMP_CY | VACANT_CY | WAGEBASECY | WHITE_CY | WHTFBASECY | WHTMBASECY | WIDOWED_CY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CA | Los Angeles County | 2094 | 1517296.0 | 2.359152e+11 | 3.196410e+11 | 3.247511e+11 | 2.224411e+12 | 35957.0 | 37602.0 | 73559.0 | 4085.699440 | 1517296.0 | 805865.0 | 711431.0 | 469649.0 | 70013.0 | 3.63 | 3.00 | 94861.0 | 660145.0 | 710686.0 | 1987624.0 | 1445895.0 | 850350.0 | 850350.0 | 450243.0 | 400107.0 | 5232399.0 | 6899087.0 | 4995839.0 | 2281483.0 | 8292844.0 | 312953.0 | 331081.0 | 4077284.0 | 384255.0 | 3858162.0 | 421330.0 | 392960.0 | 359612.0 | 328513.0 | 335961.0 | 332084.0 | 327978.0 | 310887.0 | 291733.0 | 246493.0 | 183490.0 | 131256.0 | 90178.0 | 110499.0 | 5208957.0 | 120351.0 | 255399.0 | 10288937.0 | 2130083.0 | 2353920.0 | 176419.0 | 782758.0 | 5043293.0 | 10112518.0 | 371190.0 | 243110.0 | 310104.0 | 280437.0 | 390334.0 | 554983.0 | 394431.0 | 3369582.0 | 54769.0 | 22824.0 | 45013.0 | 261906.0 | 2280356.0 | 3826.0 | 5043293.0 | 2374599.0 | 1288396.0 | 2522627.0 | 2520666.0 | 73559.0 | 06037 | 4057.8835 | 3890494.0 | 3663128.0 | 326230.0 | 351424.0 | 396276.0 | 433863.0 | 402148.0 | 368194.0 | 325765.0 | 331204.0 | 323486.0 | 307396.0 | 325231.0 | 261725.0 | 209710.0 | 149559.0 | 102402.0 | 65741.0 | 65807.0 | 5079980.0 | 8362123.0 | 3752436.0 | 35.7 | 52454.0 | 36.9 | 50.6 | 62751.0 | 34.6 | 60024.0 | 569360.0 | 2875966.0 | 7644196.0 | 251940.0 | 244185.0 | Los Angeles County | 3521307.0 | 18790.0 | 1494472.0 | 805337.0 | 234219.0 | 24674.0 | 23411.0 | 2644741.0 | 878838.0 | 5245644.0 | 2305030.0 | 205.0 | 685945.0 | 1141774.0 | 1163256.0 | 2305030.0 | 1538585.0 | 27237.0 | 27237.0 | 31563.0 | 13746.0 | 13491.0 | 639183.0 | 682505.0 | 7967778.0 | 780531.0 | 7521290.0 | 855193.0 | 795108.0 | 727806.0 | 654278.0 | 667165.0 | 655570.0 | 635374.0 | 636118.0 | 553458.0 | 456203.0 | 333049.0 | 233658.0 | 155919.0 | 176306.0 | 2535.5 | 496125.0 | 10288937.0 | 1831065.0 | {'rings': [[[-13201886.423700001, 3900000.9334... | 1300786.0 | 612414.0 | California | CA | 1.553815e+10 | 9.723835e+05 | 13 | Next Wave | 3369650.0 | 3576478.0 | 10288937.0 | 4.5 | 236560.0 | 206828.0 | 5019340.0 | 5019340.0 | 2509432.0 | 2509908.0 | 413837.0 |
| 1 | IL | Cook County | 1586 | 395712.0 | 1.284204e+11 | 1.771502e+11 | 1.797564e+11 | 1.326345e+12 | 10418.0 | 10695.0 | 21113.0 | 953.145682 | 395712.0 | 206041.0 | 189671.0 | 237028.0 | 63978.0 | 3.36 | 2.58 | 88254.0 | 660769.0 | 335311.0 | 1076607.0 | 830433.0 | 1233716.0 | 1233716.0 | 674544.0 | 559172.0 | 2791109.0 | 3607345.0 | 2599303.0 | 1216270.0 | 4087547.0 | 158205.0 | 161652.0 | 2144742.0 | 185272.0 | 2040962.0 | 217698.0 | 207243.0 | 188312.0 | 166222.0 | 164629.0 | 166570.0 | 174115.0 | 159117.0 | 162326.0 | 139839.0 | 104629.0 | 75554.0 | 54048.0 | 70522.0 | 2717277.0 | 95959.0 | 129327.0 | 5274129.0 | 1058747.0 | 1171989.0 | 90942.0 | 552050.0 | 1374256.0 | 5183187.0 | 250519.0 | 132865.0 | 186804.0 | 169815.0 | 238778.0 | 329118.0 | 243670.0 | 2007274.0 | 15131.0 | 4083.0 | 22733.0 | 66348.0 | 601231.0 | 706.0 | 1374256.0 | 664024.0 | 726645.0 | 671160.0 | 703096.0 | 21113.0 | 17031 | 945.3262 | 1963911.0 | 1860397.0 | 163128.0 | 166591.0 | 180196.0 | 214748.0 | 206465.0 | 188502.0 | 162787.0 | 160501.0 | 157805.0 | 158061.0 | 164098.0 | 141495.0 | 116039.0 | 82196.0 | 55154.0 | 35924.0 | 35961.0 | 2556852.0 | 4301056.0 | 1851961.0 | 36.6 | 47971.0 | 37.9 | 50.8 | 59718.0 | 35.4 | 76719.0 | 256912.0 | 1442885.0 | 3078944.0 | 78362.0 | 75557.0 | Cook County | 1813320.0 | 5982.0 | 391629.0 | 1210983.0 | 87571.0 | 7441.0 | 1082.0 | 2195185.0 | 237082.0 | 3899873.0 | 608672.0 | 611.0 | 394574.0 | 294742.0 | 313930.0 | 608672.0 | 1108536.0 | 1788.0 | 1788.0 | 34083.0 | 870.0 | 918.0 | 321333.0 | 328243.0 | 4108653.0 | 365468.0 | 3901359.0 | 432446.0 | 413708.0 | 376814.0 | 329009.0 | 325130.0 | 324375.0 | 332176.0 | 323215.0 | 303821.0 | 255878.0 | 186825.0 | 130708.0 | 89972.0 | 106483.0 | 5579.2 | 153919.0 | 5274129.0 | 898740.0 | {'rings': [[[-9743209.5443, 5109696.7809000015... | 690225.0 | 237923.0 | Illinois | IL | 4.445620e+09 | 7.424352e+05 | 3 | Uptown Individuals | 2007276.0 | 2230295.0 | 5274129.0 | 6.9 | 191806.0 | 223019.0 | 2859209.0 | 2859209.0 | 1452300.0 | 1406909.0 | 250105.0 |
| 2 | NY | New York County | 1136 | 212844.0 | 6.672674e+10 | 1.091191e+11 | 1.109282e+11 | 4.008776e+11 | 4542.0 | 4214.0 | 8756.0 | 22.950686 | 212844.0 | 119067.0 | 93777.0 | 48360.0 | 83690.0 | 3.02 | 2.00 | 136860.0 | 502791.0 | 1307852.0 | 345825.0 | 406878.0 | 247085.0 | 247085.0 | 133471.0 | 113614.0 | 996167.0 | 1251653.0 | 956908.0 | 314952.0 | 951099.0 | 35672.0 | 40691.0 | 760316.0 | 71461.0 | 725473.0 | 95217.0 | 86134.0 | 68890.0 | 53980.0 | 50374.0 | 50312.0 | 53400.0 | 32553.0 | 52503.0 | 49261.0 | 38526.0 | 27262.0 | 18025.0 | 22645.0 | 878322.0 | 26610.0 | 30855.0 | 1660472.0 | 339808.0 | 248875.0 | 66726.0 | 371255.0 | 441304.0 | 1593746.0 | 107793.0 | 70466.0 | 58555.0 | 49858.0 | 60209.0 | 94133.0 | 78323.0 | 797304.0 | 6592.0 | 2097.0 | 43951.0 | 36560.0 | 187987.0 | 415.0 | 441304.0 | 163702.0 | 125099.0 | 233374.0 | 207930.0 | 8756.0 | 36061 | 22.8287 | 660480.0 | 632378.0 | 37014.0 | 36186.0 | 57628.0 | 82593.0 | 79654.0 | 67405.0 | 53997.0 | 51176.0 | 48844.0 | 48003.0 | 33820.0 | 43057.0 | 37266.0 | 28231.0 | 20234.0 | 12716.0 | 11948.0 | 782150.0 | 1457619.0 | 517739.0 | 37.9 | 61201.0 | 38.3 | 48.2 | 82611.0 | 37.4 | 47793.0 | 1216203.0 | 557913.0 | 899778.0 | 40103.0 | 33176.0 | New York County | 745585.0 | 2164.0 | 210747.0 | 203134.0 | 36719.0 | 5204.0 | 506.0 | 760694.0 | 80622.0 | 1219168.0 | 193191.0 | 1859.0 | 137196.0 | 101593.0 | 91598.0 | 193191.0 | 185125.0 | 921.0 | 921.0 | 66805.0 | 484.0 | 437.0 | 72686.0 | 76877.0 | 1420796.0 | 129089.0 | 1357851.0 | 177810.0 | 165788.0 | 136295.0 | 107977.0 | 101550.0 | 99156.0 | 101403.0 | 66373.0 | 95560.0 | 86527.0 | 66757.0 | 47496.0 | 30741.0 | 34593.0 | 72736.2 | 73279.0 | 1660472.0 | 612187.0 | {'rings': [[[-8242549.350199999, 4966646.20400... | 121070.0 | 71759.0 | New York | NY | 1.024044e+08 | 1.085936e+05 | 3 | Uptown Individuals | 797312.0 | 890327.0 | 1660472.0 | 3.9 | 39259.0 | 93015.0 | 924396.0 | 924396.0 | 479062.0 | 445334.0 | 68402.0 |
| 3 | TX | Harris County | 1124 | 341640.0 | 1.132993e+11 | 1.470200e+11 | 1.487312e+11 | 1.000460e+12 | 14364.0 | 15637.0 | 30001.0 | 1748.811066 | 341640.0 | 173439.0 | 168201.0 | 193511.0 | 68781.0 | 3.49 | 2.85 | 89252.0 | 607353.0 | 251600.0 | 848003.0 | 605813.0 | 901459.0 | 901459.0 | 478023.0 | 423436.0 | 2335018.0 | 3029538.0 | 2196764.0 | 1124808.0 | 3920902.0 | 176809.0 | 152219.0 | 1773341.0 | 169057.0 | 1680385.0 | 196592.0 | 183051.0 | 171499.0 | 154056.0 | 148493.0 | 141554.0 | 141858.0 | 172608.0 | 125458.0 | 102481.0 | 70111.0 | 46215.0 | 29690.0 | 32228.0 | 2380573.0 | 98795.0 | 145111.0 | 4735852.0 | 961416.0 | 1219972.0 | 49092.0 | 345886.0 | 2035551.0 | 4686760.0 | 161383.0 | 106787.0 | 153424.0 | 159410.0 | 212729.0 | 290829.0 | 190407.0 | 1647247.0 | 21351.0 | 3892.0 | 27998.0 | 100351.0 | 703910.0 | 850.0 | 2035551.0 | 1177199.0 | 608836.0 | 994167.0 | 1041384.0 | 30001.0 | 48201 | 1703.4776 | 1723465.0 | 1625451.0 | 182838.0 | 161982.0 | 172174.0 | 206139.0 | 192088.0 | 176799.0 | 153424.0 | 146647.0 | 137454.0 | 132895.0 | 178822.0 | 114553.0 | 90812.0 | 60215.0 | 36665.0 | 20887.0 | 17674.0 | 2355279.0 | 3684970.0 | 1809576.0 | 33.5 | 50339.0 | 34.3 | 47.5 | 59417.0 | 32.7 | 72820.0 | 176533.0 | 1332331.0 | 3339578.0 | 85910.0 | 87501.0 | Harris County | 1363826.0 | 8650.0 | 337748.0 | 873461.0 | 73060.0 | 8500.0 | 2608.0 | 1396274.0 | 316268.0 | 2700301.0 | 712410.0 | 2624.0 | 229019.0 | 342097.0 | 370313.0 | 712410.0 | 888580.0 | 3458.0 | 3458.0 | 31405.0 | 1768.0 | 1690.0 | 359647.0 | 314201.0 | 3496806.0 | 341231.0 | 3305836.0 | 402731.0 | 375139.0 | 348298.0 | 307480.0 | 295140.0 | 279008.0 | 274753.0 | 351430.0 | 240011.0 | 193293.0 | 130326.0 | 82880.0 | 50577.0 | 49902.0 | 2780.1 | 173411.0 | 4735852.0 | 758671.0 | {'rings': [[[-10584325.232, 3445368.621600002]... | 606842.0 | 253587.0 | Texas | TX | 5.991513e+09 | 8.173476e+05 | 7 | Ethnic Enclaves | 1647251.0 | 1790697.0 | 4735852.0 | 5.9 | 138254.0 | 143446.0 | 2573473.0 | 2573473.0 | 1284972.0 | 1288501.0 | 161546.0 |
| 4 | AZ | Maricopa County | 844 | 184150.0 | 1.043304e+11 | 1.333034e+11 | 1.346919e+11 | 1.310493e+12 | 51929.0 | 46655.0 | 98584.0 | 9224.040205 | 184150.0 | 97892.0 | 86258.0 | 241714.0 | 64964.0 | 3.32 | 2.69 | 83005.0 | 816017.0 | 303705.0 | 872897.0 | 597653.0 | 253576.0 | 253576.0 | 124641.0 | 128935.0 | 2181523.0 | 2891837.0 | 2065771.0 | 1053223.0 | 3492063.0 | 149041.0 | 138470.0 | 1694648.0 | 151115.0 | 1606050.0 | 165009.0 | 153091.0 | 145864.0 | 134160.0 | 133211.0 | 130665.0 | 133428.0 | 147853.0 | 125740.0 | 117630.0 | 90474.0 | 62955.0 | 42348.0 | 51162.0 | 2217638.0 | 95969.0 | 119899.0 | 4387226.0 | 855726.0 | 1066649.0 | 64540.0 | 342159.0 | 1373153.0 | 4322686.0 | 163449.0 | 98771.0 | 135427.0 | 144542.0 | 215958.0 | 297023.0 | 213940.0 | 1605964.0 | 23948.0 | 4716.0 | 16585.0 | 73969.0 | 592016.0 | 1314.0 | 1373153.0 | 660605.0 | 548954.0 | 682683.0 | 690470.0 | 98584.0 | 04013 | 9200.1431 | 1624795.0 | 1531223.0 | 154750.0 | 146575.0 | 157726.0 | 172645.0 | 157924.0 | 148689.0 | 134272.0 | 132380.0 | 127236.0 | 123794.0 | 153320.0 | 111456.0 | 101867.0 | 78340.0 | 52720.0 | 33351.0 | 31426.0 | 2169588.0 | 3485723.0 | 1676208.0 | 35.8 | 50661.0 | 37.0 | 50.4 | 59691.0 | 34.7 | 102641.0 | 237947.0 | 1136843.0 | 1981147.0 | 89351.0 | 87304.0 | Maricopa County | 1200935.0 | 74636.0 | 179434.0 | 236991.0 | 102686.0 | 6019.0 | 8228.0 | 2406079.0 | 160973.0 | 3014073.0 | 598035.0 | 104.0 | 335212.0 | 292917.0 | 305118.0 | 598035.0 | 998231.0 | 9542.0 | 9542.0 | 30701.0 | 4646.0 | 4896.0 | 303791.0 | 285045.0 | 3319443.0 | 308841.0 | 3137273.0 | 337654.0 | 311015.0 | 294553.0 | 268432.0 | 265591.0 | 257901.0 | 257222.0 | 301173.0 | 237196.0 | 219497.0 | 168814.0 | 115675.0 | 75699.0 | 82588.0 | 476.9 | 176655.0 | 4387226.0 | 607760.0 | {'rings': [[[-12494641.7694, 4017468.270400002... | 711033.0 | 193382.0 | Arizona | AZ | 3.420485e+10 | 1.142138e+06 | 7 | Ethnic Enclaves | 1605991.0 | 1813056.0 | 4387226.0 | 5.3 | 115752.0 | 207065.0 | 3066684.0 | 3066684.0 | 1556262.0 | 1510422.0 | 182502.0 |
test_newcounty_df.drop(['OBJECTID','SHAPE','STATE_NAME','ST_ABBREV','NAME','Shape_Area','Shape_Length','TLIFENAME','ID','TLIFECODE'], axis=1, inplace=True)
test_newcounty_df.head()
| regionabbr | subregion | Provider_Count | AAGEBASECY | AGGDI_CY | AGGHINC_CY | AGGINC_CY | AGGNW_CY | AIFBASE_CY | AIMBASE_CY | AMERIND_CY | AREA | ASIAN_CY | ASNFBASECY | ASNMBASECY | ASSCDEG_CY | AVGDI_CY | AVGFMSZ_CY | AVGHHSZ_CY | AVGHINC_CY | AVGNW_CY | AVGVAL_CY | BABYBOOMCY | BACHDEG_CY | BAGEBASECY | BLACK_CY | BLKFBASECY | BLKMBASECY | CIVLBFR_CY | EDUCBASECY | EMP_CY | FAMHH_CY | FAMPOP_CY | FEM0_CY | FEM15_CY | FEM18UP_CY | FEM20_CY | FEM21UP_CY | FEM25_CY | FEM30_CY | FEM35_CY | FEM40_CY | FEM45_CY | FEM50_CY | FEM55_CY | FEM5_CY | FEM60_CY | FEM65_CY | FEM70_CY | FEM75_CY | FEM80_CY | FEM85_CY | FEMALES_CY | GED_CY | GENALPHACY | GENBASE_CY | GENX_CY | GENZ_CY | GQPOP_CY | GRADDEG_CY | HAGEBASECY | HHPOP_CY | HINC0_CY | HINC150_CY | HINC15_CY | HINC25_CY | HINC35_CY | HINC50_CY | HINC75_CY | HINCBASECY | HISPAI_CY | HISPASN_CY | HISPBLK_CY | HISPMLT_CY | HISPOTH_CY | HISPPI_CY | HISPPOP_CY | HISPWHT_CY | HSGRAD_CY | HSPFBASECY | HSPMBASECY | IAGEBASECY | LANDAREA | MAL18UP_CY | MAL21UP_CY | MALE0_CY | MALE15_CY | MALE20_CY | MALE25_CY | MALE30_CY | MALE35_CY | MALE40_CY | MALE45_CY | MALE50_CY | MALE55_CY | MALE5_CY | MALE60_CY | MALE65_CY | MALE70_CY | MALE75_CY | MALE80_CY | MALE85_CY | MALES_CY | MARBASE_CY | MARRIED_CY | MEDAGE_CY | MEDDI_CY | MEDFAGE_CY | MEDHHR_CY | MEDHINC_CY | MEDMAGE_CY | MEDNW_CY | MEDVAL_CY | MILLENN_CY | MINORITYCY | MLTFBASECY | MLTMBASECY | NEVMARR_CY | NHSPAI_CY | NHSPASN_CY | NHSPBLK_CY | NHSPMLT_CY | NHSPOTH_CY | NHSPPI_CY | NHSPWHT_CY | NOHS_CY | NONHISP_CY | OAGEBASECY | OLDRGENSCY | OTHFBASECY | OTHMBASECY | OTHRACE_CY | OWNER_CY | PACIFIC_CY | PAGEBASECY | PCI_CY | PIFBASE_CY | PIMBASE_CY | POP0_CY | POP15_CY | POP18UP_CY | POP20_CY | POP21UP_CY | POP25_CY | POP30_CY | POP35_CY | POP40_CY | POP45_CY | POP50_CY | POP55_CY | POP5_CY | POP60_CY | POP65_CY | POP70_CY | POP75_CY | POP80_CY | POP85_CY | POPDENS_CY | RACE2UP_CY | RACEBASECY | RENTER_CY | SMCOLL_CY | SOMEHS_CY | TOTHH_CY | TOTHU_CY | TOTPOP_CY | UNEMPRT_CY | UNEMP_CY | VACANT_CY | WAGEBASECY | WHITE_CY | WHTFBASECY | WHTMBASECY | WIDOWED_CY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CA | Los Angeles County | 2094 | 1517296.0 | 2.359152e+11 | 3.196410e+11 | 3.247511e+11 | 2.224411e+12 | 35957.0 | 37602.0 | 73559.0 | 4085.699440 | 1517296.0 | 805865.0 | 711431.0 | 469649.0 | 70013.0 | 3.63 | 3.00 | 94861.0 | 660145.0 | 710686.0 | 1987624.0 | 1445895.0 | 850350.0 | 850350.0 | 450243.0 | 400107.0 | 5232399.0 | 6899087.0 | 4995839.0 | 2281483.0 | 8292844.0 | 312953.0 | 331081.0 | 4077284.0 | 384255.0 | 3858162.0 | 421330.0 | 392960.0 | 359612.0 | 328513.0 | 335961.0 | 332084.0 | 327978.0 | 310887.0 | 291733.0 | 246493.0 | 183490.0 | 131256.0 | 90178.0 | 110499.0 | 5208957.0 | 120351.0 | 255399.0 | 10288937.0 | 2130083.0 | 2353920.0 | 176419.0 | 782758.0 | 5043293.0 | 10112518.0 | 371190.0 | 243110.0 | 310104.0 | 280437.0 | 390334.0 | 554983.0 | 394431.0 | 3369582.0 | 54769.0 | 22824.0 | 45013.0 | 261906.0 | 2280356.0 | 3826.0 | 5043293.0 | 2374599.0 | 1288396.0 | 2522627.0 | 2520666.0 | 73559.0 | 4057.8835 | 3890494.0 | 3663128.0 | 326230.0 | 351424.0 | 396276.0 | 433863.0 | 402148.0 | 368194.0 | 325765.0 | 331204.0 | 323486.0 | 307396.0 | 325231.0 | 261725.0 | 209710.0 | 149559.0 | 102402.0 | 65741.0 | 65807.0 | 5079980.0 | 8362123.0 | 3752436.0 | 35.7 | 52454.0 | 36.9 | 50.6 | 62751.0 | 34.6 | 60024.0 | 569360.0 | 2875966.0 | 7644196.0 | 251940.0 | 244185.0 | 3521307.0 | 18790.0 | 1494472.0 | 805337.0 | 234219.0 | 24674.0 | 23411.0 | 2644741.0 | 878838.0 | 5245644.0 | 2305030.0 | 685945.0 | 1141774.0 | 1163256.0 | 2305030.0 | 1538585.0 | 27237.0 | 27237.0 | 31563.0 | 13746.0 | 13491.0 | 639183.0 | 682505.0 | 7967778.0 | 780531.0 | 7521290.0 | 855193.0 | 795108.0 | 727806.0 | 654278.0 | 667165.0 | 655570.0 | 635374.0 | 636118.0 | 553458.0 | 456203.0 | 333049.0 | 233658.0 | 155919.0 | 176306.0 | 2535.5 | 496125.0 | 10288937.0 | 1831065.0 | 1300786.0 | 612414.0 | 3369650.0 | 3576478.0 | 10288937.0 | 4.5 | 236560.0 | 206828.0 | 5019340.0 | 5019340.0 | 2509432.0 | 2509908.0 | 413837.0 |
| 1 | IL | Cook County | 1586 | 395712.0 | 1.284204e+11 | 1.771502e+11 | 1.797564e+11 | 1.326345e+12 | 10418.0 | 10695.0 | 21113.0 | 953.145682 | 395712.0 | 206041.0 | 189671.0 | 237028.0 | 63978.0 | 3.36 | 2.58 | 88254.0 | 660769.0 | 335311.0 | 1076607.0 | 830433.0 | 1233716.0 | 1233716.0 | 674544.0 | 559172.0 | 2791109.0 | 3607345.0 | 2599303.0 | 1216270.0 | 4087547.0 | 158205.0 | 161652.0 | 2144742.0 | 185272.0 | 2040962.0 | 217698.0 | 207243.0 | 188312.0 | 166222.0 | 164629.0 | 166570.0 | 174115.0 | 159117.0 | 162326.0 | 139839.0 | 104629.0 | 75554.0 | 54048.0 | 70522.0 | 2717277.0 | 95959.0 | 129327.0 | 5274129.0 | 1058747.0 | 1171989.0 | 90942.0 | 552050.0 | 1374256.0 | 5183187.0 | 250519.0 | 132865.0 | 186804.0 | 169815.0 | 238778.0 | 329118.0 | 243670.0 | 2007274.0 | 15131.0 | 4083.0 | 22733.0 | 66348.0 | 601231.0 | 706.0 | 1374256.0 | 664024.0 | 726645.0 | 671160.0 | 703096.0 | 21113.0 | 945.3262 | 1963911.0 | 1860397.0 | 163128.0 | 166591.0 | 180196.0 | 214748.0 | 206465.0 | 188502.0 | 162787.0 | 160501.0 | 157805.0 | 158061.0 | 164098.0 | 141495.0 | 116039.0 | 82196.0 | 55154.0 | 35924.0 | 35961.0 | 2556852.0 | 4301056.0 | 1851961.0 | 36.6 | 47971.0 | 37.9 | 50.8 | 59718.0 | 35.4 | 76719.0 | 256912.0 | 1442885.0 | 3078944.0 | 78362.0 | 75557.0 | 1813320.0 | 5982.0 | 391629.0 | 1210983.0 | 87571.0 | 7441.0 | 1082.0 | 2195185.0 | 237082.0 | 3899873.0 | 608672.0 | 394574.0 | 294742.0 | 313930.0 | 608672.0 | 1108536.0 | 1788.0 | 1788.0 | 34083.0 | 870.0 | 918.0 | 321333.0 | 328243.0 | 4108653.0 | 365468.0 | 3901359.0 | 432446.0 | 413708.0 | 376814.0 | 329009.0 | 325130.0 | 324375.0 | 332176.0 | 323215.0 | 303821.0 | 255878.0 | 186825.0 | 130708.0 | 89972.0 | 106483.0 | 5579.2 | 153919.0 | 5274129.0 | 898740.0 | 690225.0 | 237923.0 | 2007276.0 | 2230295.0 | 5274129.0 | 6.9 | 191806.0 | 223019.0 | 2859209.0 | 2859209.0 | 1452300.0 | 1406909.0 | 250105.0 |
| 2 | NY | New York County | 1136 | 212844.0 | 6.672674e+10 | 1.091191e+11 | 1.109282e+11 | 4.008776e+11 | 4542.0 | 4214.0 | 8756.0 | 22.950686 | 212844.0 | 119067.0 | 93777.0 | 48360.0 | 83690.0 | 3.02 | 2.00 | 136860.0 | 502791.0 | 1307852.0 | 345825.0 | 406878.0 | 247085.0 | 247085.0 | 133471.0 | 113614.0 | 996167.0 | 1251653.0 | 956908.0 | 314952.0 | 951099.0 | 35672.0 | 40691.0 | 760316.0 | 71461.0 | 725473.0 | 95217.0 | 86134.0 | 68890.0 | 53980.0 | 50374.0 | 50312.0 | 53400.0 | 32553.0 | 52503.0 | 49261.0 | 38526.0 | 27262.0 | 18025.0 | 22645.0 | 878322.0 | 26610.0 | 30855.0 | 1660472.0 | 339808.0 | 248875.0 | 66726.0 | 371255.0 | 441304.0 | 1593746.0 | 107793.0 | 70466.0 | 58555.0 | 49858.0 | 60209.0 | 94133.0 | 78323.0 | 797304.0 | 6592.0 | 2097.0 | 43951.0 | 36560.0 | 187987.0 | 415.0 | 441304.0 | 163702.0 | 125099.0 | 233374.0 | 207930.0 | 8756.0 | 22.8287 | 660480.0 | 632378.0 | 37014.0 | 36186.0 | 57628.0 | 82593.0 | 79654.0 | 67405.0 | 53997.0 | 51176.0 | 48844.0 | 48003.0 | 33820.0 | 43057.0 | 37266.0 | 28231.0 | 20234.0 | 12716.0 | 11948.0 | 782150.0 | 1457619.0 | 517739.0 | 37.9 | 61201.0 | 38.3 | 48.2 | 82611.0 | 37.4 | 47793.0 | 1216203.0 | 557913.0 | 899778.0 | 40103.0 | 33176.0 | 745585.0 | 2164.0 | 210747.0 | 203134.0 | 36719.0 | 5204.0 | 506.0 | 760694.0 | 80622.0 | 1219168.0 | 193191.0 | 137196.0 | 101593.0 | 91598.0 | 193191.0 | 185125.0 | 921.0 | 921.0 | 66805.0 | 484.0 | 437.0 | 72686.0 | 76877.0 | 1420796.0 | 129089.0 | 1357851.0 | 177810.0 | 165788.0 | 136295.0 | 107977.0 | 101550.0 | 99156.0 | 101403.0 | 66373.0 | 95560.0 | 86527.0 | 66757.0 | 47496.0 | 30741.0 | 34593.0 | 72736.2 | 73279.0 | 1660472.0 | 612187.0 | 121070.0 | 71759.0 | 797312.0 | 890327.0 | 1660472.0 | 3.9 | 39259.0 | 93015.0 | 924396.0 | 924396.0 | 479062.0 | 445334.0 | 68402.0 |
| 3 | TX | Harris County | 1124 | 341640.0 | 1.132993e+11 | 1.470200e+11 | 1.487312e+11 | 1.000460e+12 | 14364.0 | 15637.0 | 30001.0 | 1748.811066 | 341640.0 | 173439.0 | 168201.0 | 193511.0 | 68781.0 | 3.49 | 2.85 | 89252.0 | 607353.0 | 251600.0 | 848003.0 | 605813.0 | 901459.0 | 901459.0 | 478023.0 | 423436.0 | 2335018.0 | 3029538.0 | 2196764.0 | 1124808.0 | 3920902.0 | 176809.0 | 152219.0 | 1773341.0 | 169057.0 | 1680385.0 | 196592.0 | 183051.0 | 171499.0 | 154056.0 | 148493.0 | 141554.0 | 141858.0 | 172608.0 | 125458.0 | 102481.0 | 70111.0 | 46215.0 | 29690.0 | 32228.0 | 2380573.0 | 98795.0 | 145111.0 | 4735852.0 | 961416.0 | 1219972.0 | 49092.0 | 345886.0 | 2035551.0 | 4686760.0 | 161383.0 | 106787.0 | 153424.0 | 159410.0 | 212729.0 | 290829.0 | 190407.0 | 1647247.0 | 21351.0 | 3892.0 | 27998.0 | 100351.0 | 703910.0 | 850.0 | 2035551.0 | 1177199.0 | 608836.0 | 994167.0 | 1041384.0 | 30001.0 | 1703.4776 | 1723465.0 | 1625451.0 | 182838.0 | 161982.0 | 172174.0 | 206139.0 | 192088.0 | 176799.0 | 153424.0 | 146647.0 | 137454.0 | 132895.0 | 178822.0 | 114553.0 | 90812.0 | 60215.0 | 36665.0 | 20887.0 | 17674.0 | 2355279.0 | 3684970.0 | 1809576.0 | 33.5 | 50339.0 | 34.3 | 47.5 | 59417.0 | 32.7 | 72820.0 | 176533.0 | 1332331.0 | 3339578.0 | 85910.0 | 87501.0 | 1363826.0 | 8650.0 | 337748.0 | 873461.0 | 73060.0 | 8500.0 | 2608.0 | 1396274.0 | 316268.0 | 2700301.0 | 712410.0 | 229019.0 | 342097.0 | 370313.0 | 712410.0 | 888580.0 | 3458.0 | 3458.0 | 31405.0 | 1768.0 | 1690.0 | 359647.0 | 314201.0 | 3496806.0 | 341231.0 | 3305836.0 | 402731.0 | 375139.0 | 348298.0 | 307480.0 | 295140.0 | 279008.0 | 274753.0 | 351430.0 | 240011.0 | 193293.0 | 130326.0 | 82880.0 | 50577.0 | 49902.0 | 2780.1 | 173411.0 | 4735852.0 | 758671.0 | 606842.0 | 253587.0 | 1647251.0 | 1790697.0 | 4735852.0 | 5.9 | 138254.0 | 143446.0 | 2573473.0 | 2573473.0 | 1284972.0 | 1288501.0 | 161546.0 |
| 4 | AZ | Maricopa County | 844 | 184150.0 | 1.043304e+11 | 1.333034e+11 | 1.346919e+11 | 1.310493e+12 | 51929.0 | 46655.0 | 98584.0 | 9224.040205 | 184150.0 | 97892.0 | 86258.0 | 241714.0 | 64964.0 | 3.32 | 2.69 | 83005.0 | 816017.0 | 303705.0 | 872897.0 | 597653.0 | 253576.0 | 253576.0 | 124641.0 | 128935.0 | 2181523.0 | 2891837.0 | 2065771.0 | 1053223.0 | 3492063.0 | 149041.0 | 138470.0 | 1694648.0 | 151115.0 | 1606050.0 | 165009.0 | 153091.0 | 145864.0 | 134160.0 | 133211.0 | 130665.0 | 133428.0 | 147853.0 | 125740.0 | 117630.0 | 90474.0 | 62955.0 | 42348.0 | 51162.0 | 2217638.0 | 95969.0 | 119899.0 | 4387226.0 | 855726.0 | 1066649.0 | 64540.0 | 342159.0 | 1373153.0 | 4322686.0 | 163449.0 | 98771.0 | 135427.0 | 144542.0 | 215958.0 | 297023.0 | 213940.0 | 1605964.0 | 23948.0 | 4716.0 | 16585.0 | 73969.0 | 592016.0 | 1314.0 | 1373153.0 | 660605.0 | 548954.0 | 682683.0 | 690470.0 | 98584.0 | 9200.1431 | 1624795.0 | 1531223.0 | 154750.0 | 146575.0 | 157726.0 | 172645.0 | 157924.0 | 148689.0 | 134272.0 | 132380.0 | 127236.0 | 123794.0 | 153320.0 | 111456.0 | 101867.0 | 78340.0 | 52720.0 | 33351.0 | 31426.0 | 2169588.0 | 3485723.0 | 1676208.0 | 35.8 | 50661.0 | 37.0 | 50.4 | 59691.0 | 34.7 | 102641.0 | 237947.0 | 1136843.0 | 1981147.0 | 89351.0 | 87304.0 | 1200935.0 | 74636.0 | 179434.0 | 236991.0 | 102686.0 | 6019.0 | 8228.0 | 2406079.0 | 160973.0 | 3014073.0 | 598035.0 | 335212.0 | 292917.0 | 305118.0 | 598035.0 | 998231.0 | 9542.0 | 9542.0 | 30701.0 | 4646.0 | 4896.0 | 303791.0 | 285045.0 | 3319443.0 | 308841.0 | 3137273.0 | 337654.0 | 311015.0 | 294553.0 | 268432.0 | 265591.0 | 257901.0 | 257222.0 | 301173.0 | 237196.0 | 219497.0 | 168814.0 | 115675.0 | 75699.0 | 82588.0 | 476.9 | 176655.0 | 4387226.0 | 607760.0 | 711033.0 | 193382.0 | 1605991.0 | 1813056.0 | 4387226.0 | 5.3 | 115752.0 | 207065.0 | 3066684.0 | 3066684.0 | 1556262.0 | 1510422.0 | 182502.0 |
# Change Provider Count to Float
test_newcounty_df['Provider_Count'] = test_newcounty_df['Provider_Count'].astype(float)
test_newcounty_df.head()
| regionabbr | subregion | Provider_Count | AAGEBASECY | AGGDI_CY | AGGHINC_CY | AGGINC_CY | AGGNW_CY | AIFBASE_CY | AIMBASE_CY | AMERIND_CY | AREA | ASIAN_CY | ASNFBASECY | ASNMBASECY | ASSCDEG_CY | AVGDI_CY | AVGFMSZ_CY | AVGHHSZ_CY | AVGHINC_CY | AVGNW_CY | AVGVAL_CY | BABYBOOMCY | BACHDEG_CY | BAGEBASECY | BLACK_CY | BLKFBASECY | BLKMBASECY | CIVLBFR_CY | EDUCBASECY | EMP_CY | FAMHH_CY | FAMPOP_CY | FEM0_CY | FEM15_CY | FEM18UP_CY | FEM20_CY | FEM21UP_CY | FEM25_CY | FEM30_CY | FEM35_CY | FEM40_CY | FEM45_CY | FEM50_CY | FEM55_CY | FEM5_CY | FEM60_CY | FEM65_CY | FEM70_CY | FEM75_CY | FEM80_CY | FEM85_CY | FEMALES_CY | GED_CY | GENALPHACY | GENBASE_CY | GENX_CY | GENZ_CY | GRADDEG_CY | HAGEBASECY | HHPOP_CY | HINC0_CY | HINC150_CY | HINC15_CY | HINC25_CY | HINC35_CY | HINC50_CY | HINC75_CY | HINCBASECY | HISPMLT_CY | HISPOTH_CY | HISPPOP_CY | HISPWHT_CY | HSGRAD_CY | HSPFBASECY | HSPMBASECY | IAGEBASECY | LANDAREA | MAL18UP_CY | MAL21UP_CY | MALE0_CY | MALE15_CY | MALE20_CY | MALE25_CY | MALE30_CY | MALE35_CY | MALE40_CY | MALE45_CY | MALE50_CY | MALE55_CY | MALE5_CY | MALE60_CY | MALE65_CY | MALE70_CY | MALE75_CY | MALE80_CY | MALE85_CY | MALES_CY | MARBASE_CY | MARRIED_CY | MEDAGE_CY | MEDDI_CY | MEDFAGE_CY | MEDHHR_CY | MEDHINC_CY | MEDMAGE_CY | MEDNW_CY | MEDVAL_CY | MILLENN_CY | MINORITYCY | MLTFBASECY | MLTMBASECY | NEVMARR_CY | NHSPAI_CY | NHSPASN_CY | NHSPBLK_CY | NHSPMLT_CY | NHSPWHT_CY | NOHS_CY | NONHISP_CY | OAGEBASECY | OLDRGENSCY | OTHFBASECY | OTHRACE_CY | OWNER_CY | PCI_CY | POP0_CY | POP15_CY | POP18UP_CY | POP20_CY | POP21UP_CY | POP25_CY | POP30_CY | POP35_CY | POP40_CY | POP45_CY | POP50_CY | POP55_CY | POP5_CY | POP60_CY | POP65_CY | POP70_CY | POP75_CY | POP80_CY | POP85_CY | POPDENS_CY | RACE2UP_CY | RACEBASECY | RENTER_CY | SMCOLL_CY | SOMEHS_CY | TOTHH_CY | TOTHU_CY | TOTPOP_CY | UNEMPRT_CY | UNEMP_CY | VACANT_CY | WAGEBASECY | WHITE_CY | WHTFBASECY | WHTMBASECY | WIDOWED_CY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CA | Los Angeles County | 2094.0 | 1517296.0 | 2.359152e+11 | 3.196410e+11 | 3.247511e+11 | 2.224411e+12 | 35957.0 | 37602.0 | 73559.0 | 4085.699440 | 1517296.0 | 805865.0 | 711431.0 | 469649.0 | 70013.0 | 3.63 | 3.00 | 94861.0 | 660145.0 | 710686.0 | 1987624.0 | 1445895.0 | 850350.0 | 850350.0 | 450243.0 | 400107.0 | 5232399.0 | 6899087.0 | 4995839.0 | 2281483.0 | 8292844.0 | 312953.0 | 331081.0 | 4077284.0 | 384255.0 | 3858162.0 | 421330.0 | 392960.0 | 359612.0 | 328513.0 | 335961.0 | 332084.0 | 327978.0 | 310887.0 | 291733.0 | 246493.0 | 183490.0 | 131256.0 | 90178.0 | 110499.0 | 5208957.0 | 120351.0 | 255399.0 | 10288937.0 | 2130083.0 | 2353920.0 | 782758.0 | 5043293.0 | 10112518.0 | 371190.0 | 243110.0 | 310104.0 | 280437.0 | 390334.0 | 554983.0 | 394431.0 | 3369582.0 | 261906.0 | 2280356.0 | 5043293.0 | 2374599.0 | 1288396.0 | 2522627.0 | 2520666.0 | 73559.0 | 4057.8835 | 3890494.0 | 3663128.0 | 326230.0 | 351424.0 | 396276.0 | 433863.0 | 402148.0 | 368194.0 | 325765.0 | 331204.0 | 323486.0 | 307396.0 | 325231.0 | 261725.0 | 209710.0 | 149559.0 | 102402.0 | 65741.0 | 65807.0 | 5079980.0 | 8362123.0 | 3752436.0 | 35.7 | 52454.0 | 36.9 | 50.6 | 62751.0 | 34.6 | 60024.0 | 569360.0 | 2875966.0 | 7644196.0 | 251940.0 | 244185.0 | 3521307.0 | 18790.0 | 1494472.0 | 805337.0 | 234219.0 | 2644741.0 | 878838.0 | 5245644.0 | 2305030.0 | 685945.0 | 1141774.0 | 2305030.0 | 1538585.0 | 31563.0 | 639183.0 | 682505.0 | 7967778.0 | 780531.0 | 7521290.0 | 855193.0 | 795108.0 | 727806.0 | 654278.0 | 667165.0 | 655570.0 | 635374.0 | 636118.0 | 553458.0 | 456203.0 | 333049.0 | 233658.0 | 155919.0 | 176306.0 | 2535.5 | 496125.0 | 10288937.0 | 1831065.0 | 1300786.0 | 612414.0 | 3369650.0 | 3576478.0 | 10288937.0 | 4.5 | 236560.0 | 206828.0 | 5019340.0 | 5019340.0 | 2509432.0 | 2509908.0 | 413837.0 |
| 1 | IL | Cook County | 1586.0 | 395712.0 | 1.284204e+11 | 1.771502e+11 | 1.797564e+11 | 1.326345e+12 | 10418.0 | 10695.0 | 21113.0 | 953.145682 | 395712.0 | 206041.0 | 189671.0 | 237028.0 | 63978.0 | 3.36 | 2.58 | 88254.0 | 660769.0 | 335311.0 | 1076607.0 | 830433.0 | 1233716.0 | 1233716.0 | 674544.0 | 559172.0 | 2791109.0 | 3607345.0 | 2599303.0 | 1216270.0 | 4087547.0 | 158205.0 | 161652.0 | 2144742.0 | 185272.0 | 2040962.0 | 217698.0 | 207243.0 | 188312.0 | 166222.0 | 164629.0 | 166570.0 | 174115.0 | 159117.0 | 162326.0 | 139839.0 | 104629.0 | 75554.0 | 54048.0 | 70522.0 | 2717277.0 | 95959.0 | 129327.0 | 5274129.0 | 1058747.0 | 1171989.0 | 552050.0 | 1374256.0 | 5183187.0 | 250519.0 | 132865.0 | 186804.0 | 169815.0 | 238778.0 | 329118.0 | 243670.0 | 2007274.0 | 66348.0 | 601231.0 | 1374256.0 | 664024.0 | 726645.0 | 671160.0 | 703096.0 | 21113.0 | 945.3262 | 1963911.0 | 1860397.0 | 163128.0 | 166591.0 | 180196.0 | 214748.0 | 206465.0 | 188502.0 | 162787.0 | 160501.0 | 157805.0 | 158061.0 | 164098.0 | 141495.0 | 116039.0 | 82196.0 | 55154.0 | 35924.0 | 35961.0 | 2556852.0 | 4301056.0 | 1851961.0 | 36.6 | 47971.0 | 37.9 | 50.8 | 59718.0 | 35.4 | 76719.0 | 256912.0 | 1442885.0 | 3078944.0 | 78362.0 | 75557.0 | 1813320.0 | 5982.0 | 391629.0 | 1210983.0 | 87571.0 | 2195185.0 | 237082.0 | 3899873.0 | 608672.0 | 394574.0 | 294742.0 | 608672.0 | 1108536.0 | 34083.0 | 321333.0 | 328243.0 | 4108653.0 | 365468.0 | 3901359.0 | 432446.0 | 413708.0 | 376814.0 | 329009.0 | 325130.0 | 324375.0 | 332176.0 | 323215.0 | 303821.0 | 255878.0 | 186825.0 | 130708.0 | 89972.0 | 106483.0 | 5579.2 | 153919.0 | 5274129.0 | 898740.0 | 690225.0 | 237923.0 | 2007276.0 | 2230295.0 | 5274129.0 | 6.9 | 191806.0 | 223019.0 | 2859209.0 | 2859209.0 | 1452300.0 | 1406909.0 | 250105.0 |
| 2 | NY | New York County | 1136.0 | 212844.0 | 6.672674e+10 | 1.091191e+11 | 1.109282e+11 | 4.008776e+11 | 4542.0 | 4214.0 | 8756.0 | 22.950686 | 212844.0 | 119067.0 | 93777.0 | 48360.0 | 83690.0 | 3.02 | 2.00 | 136860.0 | 502791.0 | 1307852.0 | 345825.0 | 406878.0 | 247085.0 | 247085.0 | 133471.0 | 113614.0 | 996167.0 | 1251653.0 | 956908.0 | 314952.0 | 951099.0 | 35672.0 | 40691.0 | 760316.0 | 71461.0 | 725473.0 | 95217.0 | 86134.0 | 68890.0 | 53980.0 | 50374.0 | 50312.0 | 53400.0 | 32553.0 | 52503.0 | 49261.0 | 38526.0 | 27262.0 | 18025.0 | 22645.0 | 878322.0 | 26610.0 | 30855.0 | 1660472.0 | 339808.0 | 248875.0 | 371255.0 | 441304.0 | 1593746.0 | 107793.0 | 70466.0 | 58555.0 | 49858.0 | 60209.0 | 94133.0 | 78323.0 | 797304.0 | 36560.0 | 187987.0 | 441304.0 | 163702.0 | 125099.0 | 233374.0 | 207930.0 | 8756.0 | 22.8287 | 660480.0 | 632378.0 | 37014.0 | 36186.0 | 57628.0 | 82593.0 | 79654.0 | 67405.0 | 53997.0 | 51176.0 | 48844.0 | 48003.0 | 33820.0 | 43057.0 | 37266.0 | 28231.0 | 20234.0 | 12716.0 | 11948.0 | 782150.0 | 1457619.0 | 517739.0 | 37.9 | 61201.0 | 38.3 | 48.2 | 82611.0 | 37.4 | 47793.0 | 1216203.0 | 557913.0 | 899778.0 | 40103.0 | 33176.0 | 745585.0 | 2164.0 | 210747.0 | 203134.0 | 36719.0 | 760694.0 | 80622.0 | 1219168.0 | 193191.0 | 137196.0 | 101593.0 | 193191.0 | 185125.0 | 66805.0 | 72686.0 | 76877.0 | 1420796.0 | 129089.0 | 1357851.0 | 177810.0 | 165788.0 | 136295.0 | 107977.0 | 101550.0 | 99156.0 | 101403.0 | 66373.0 | 95560.0 | 86527.0 | 66757.0 | 47496.0 | 30741.0 | 34593.0 | 72736.2 | 73279.0 | 1660472.0 | 612187.0 | 121070.0 | 71759.0 | 797312.0 | 890327.0 | 1660472.0 | 3.9 | 39259.0 | 93015.0 | 924396.0 | 924396.0 | 479062.0 | 445334.0 | 68402.0 |
| 3 | TX | Harris County | 1124.0 | 341640.0 | 1.132993e+11 | 1.470200e+11 | 1.487312e+11 | 1.000460e+12 | 14364.0 | 15637.0 | 30001.0 | 1748.811066 | 341640.0 | 173439.0 | 168201.0 | 193511.0 | 68781.0 | 3.49 | 2.85 | 89252.0 | 607353.0 | 251600.0 | 848003.0 | 605813.0 | 901459.0 | 901459.0 | 478023.0 | 423436.0 | 2335018.0 | 3029538.0 | 2196764.0 | 1124808.0 | 3920902.0 | 176809.0 | 152219.0 | 1773341.0 | 169057.0 | 1680385.0 | 196592.0 | 183051.0 | 171499.0 | 154056.0 | 148493.0 | 141554.0 | 141858.0 | 172608.0 | 125458.0 | 102481.0 | 70111.0 | 46215.0 | 29690.0 | 32228.0 | 2380573.0 | 98795.0 | 145111.0 | 4735852.0 | 961416.0 | 1219972.0 | 345886.0 | 2035551.0 | 4686760.0 | 161383.0 | 106787.0 | 153424.0 | 159410.0 | 212729.0 | 290829.0 | 190407.0 | 1647247.0 | 100351.0 | 703910.0 | 2035551.0 | 1177199.0 | 608836.0 | 994167.0 | 1041384.0 | 30001.0 | 1703.4776 | 1723465.0 | 1625451.0 | 182838.0 | 161982.0 | 172174.0 | 206139.0 | 192088.0 | 176799.0 | 153424.0 | 146647.0 | 137454.0 | 132895.0 | 178822.0 | 114553.0 | 90812.0 | 60215.0 | 36665.0 | 20887.0 | 17674.0 | 2355279.0 | 3684970.0 | 1809576.0 | 33.5 | 50339.0 | 34.3 | 47.5 | 59417.0 | 32.7 | 72820.0 | 176533.0 | 1332331.0 | 3339578.0 | 85910.0 | 87501.0 | 1363826.0 | 8650.0 | 337748.0 | 873461.0 | 73060.0 | 1396274.0 | 316268.0 | 2700301.0 | 712410.0 | 229019.0 | 342097.0 | 712410.0 | 888580.0 | 31405.0 | 359647.0 | 314201.0 | 3496806.0 | 341231.0 | 3305836.0 | 402731.0 | 375139.0 | 348298.0 | 307480.0 | 295140.0 | 279008.0 | 274753.0 | 351430.0 | 240011.0 | 193293.0 | 130326.0 | 82880.0 | 50577.0 | 49902.0 | 2780.1 | 173411.0 | 4735852.0 | 758671.0 | 606842.0 | 253587.0 | 1647251.0 | 1790697.0 | 4735852.0 | 5.9 | 138254.0 | 143446.0 | 2573473.0 | 2573473.0 | 1284972.0 | 1288501.0 | 161546.0 |
| 4 | AZ | Maricopa County | 844.0 | 184150.0 | 1.043304e+11 | 1.333034e+11 | 1.346919e+11 | 1.310493e+12 | 51929.0 | 46655.0 | 98584.0 | 9224.040205 | 184150.0 | 97892.0 | 86258.0 | 241714.0 | 64964.0 | 3.32 | 2.69 | 83005.0 | 816017.0 | 303705.0 | 872897.0 | 597653.0 | 253576.0 | 253576.0 | 124641.0 | 128935.0 | 2181523.0 | 2891837.0 | 2065771.0 | 1053223.0 | 3492063.0 | 149041.0 | 138470.0 | 1694648.0 | 151115.0 | 1606050.0 | 165009.0 | 153091.0 | 145864.0 | 134160.0 | 133211.0 | 130665.0 | 133428.0 | 147853.0 | 125740.0 | 117630.0 | 90474.0 | 62955.0 | 42348.0 | 51162.0 | 2217638.0 | 95969.0 | 119899.0 | 4387226.0 | 855726.0 | 1066649.0 | 342159.0 | 1373153.0 | 4322686.0 | 163449.0 | 98771.0 | 135427.0 | 144542.0 | 215958.0 | 297023.0 | 213940.0 | 1605964.0 | 73969.0 | 592016.0 | 1373153.0 | 660605.0 | 548954.0 | 682683.0 | 690470.0 | 98584.0 | 9200.1431 | 1624795.0 | 1531223.0 | 154750.0 | 146575.0 | 157726.0 | 172645.0 | 157924.0 | 148689.0 | 134272.0 | 132380.0 | 127236.0 | 123794.0 | 153320.0 | 111456.0 | 101867.0 | 78340.0 | 52720.0 | 33351.0 | 31426.0 | 2169588.0 | 3485723.0 | 1676208.0 | 35.8 | 50661.0 | 37.0 | 50.4 | 59691.0 | 34.7 | 102641.0 | 237947.0 | 1136843.0 | 1981147.0 | 89351.0 | 87304.0 | 1200935.0 | 74636.0 | 179434.0 | 236991.0 | 102686.0 | 2406079.0 | 160973.0 | 3014073.0 | 598035.0 | 335212.0 | 292917.0 | 598035.0 | 998231.0 | 30701.0 | 303791.0 | 285045.0 | 3319443.0 | 308841.0 | 3137273.0 | 337654.0 | 311015.0 | 294553.0 | 268432.0 | 265591.0 | 257901.0 | 257222.0 | 301173.0 | 237196.0 | 219497.0 | 168814.0 | 115675.0 | 75699.0 | 82588.0 | 476.9 | 176655.0 | 4387226.0 | 607760.0 | 711033.0 | 193382.0 | 1605991.0 | 1813056.0 | 4387226.0 | 5.3 | 115752.0 | 207065.0 | 3066684.0 | 3066684.0 | 1556262.0 | 1510422.0 | 182502.0 |
# Any missing values in the data
test_newcounty_df.isnull().sum().sum()
21375
# Columns with missing values
test_newcounty_df.isnull().sum()
regionabbr 0
subregion 0
Provider_Count 0
AAGEBASECY 125
AGGDI_CY 125
AGGHINC_CY 125
AGGINC_CY 125
AGGNW_CY 125
AIFBASE_CY 125
AIMBASE_CY 125
AMERIND_CY 125
AREA 125
ASIAN_CY 125
ASNFBASECY 125
ASNMBASECY 125
ASSCDEG_CY 125
AVGDI_CY 125
AVGFMSZ_CY 125
AVGHHSZ_CY 125
AVGHINC_CY 125
AVGNW_CY 125
AVGVAL_CY 125
BABYBOOMCY 125
BACHDEG_CY 125
BAGEBASECY 125
BLACK_CY 125
BLKFBASECY 125
BLKMBASECY 125
CIVLBFR_CY 125
EDUCBASECY 125
...
POP30_CY 125
POP35_CY 125
POP40_CY 125
POP45_CY 125
POP50_CY 125
POP55_CY 125
POP5_CY 125
POP60_CY 125
POP65_CY 125
POP70_CY 125
POP75_CY 125
POP80_CY 125
POP85_CY 125
POPDENS_CY 125
RACE2UP_CY 125
RACEBASECY 125
RENTER_CY 125
SMCOLL_CY 125
SOMEHS_CY 125
TOTHH_CY 125
TOTHU_CY 125
TOTPOP_CY 125
UNEMPRT_CY 125
UNEMP_CY 125
VACANT_CY 125
WAGEBASECY 125
WHITE_CY 125
WHTFBASECY 125
WHTMBASECY 125
WIDOWED_CY 125
Length: 174, dtype: int64
# Dataframe of rows and columns with null
null_data = test_newcounty_df[test_newcounty_df.isnull().any(axis=1)]
null_data.head()
| regionabbr | subregion | Provider_Count | AAGEBASECY | AGGDI_CY | AGGHINC_CY | AGGINC_CY | AGGNW_CY | AIFBASE_CY | AIMBASE_CY | AMERIND_CY | AREA | ASIAN_CY | ASNFBASECY | ASNMBASECY | ASSCDEG_CY | AVGDI_CY | AVGFMSZ_CY | AVGHHSZ_CY | AVGHINC_CY | AVGNW_CY | AVGVAL_CY | BABYBOOMCY | BACHDEG_CY | BAGEBASECY | BLACK_CY | BLKFBASECY | BLKMBASECY | CIVLBFR_CY | EDUCBASECY | EMP_CY | FAMHH_CY | FAMPOP_CY | FEM0_CY | FEM15_CY | FEM18UP_CY | FEM20_CY | FEM21UP_CY | FEM25_CY | FEM30_CY | FEM35_CY | FEM40_CY | FEM45_CY | FEM50_CY | FEM55_CY | FEM5_CY | FEM60_CY | FEM65_CY | FEM70_CY | FEM75_CY | FEM80_CY | FEM85_CY | FEMALES_CY | GED_CY | GENALPHACY | GENBASE_CY | GENX_CY | GENZ_CY | GQPOP_CY | GRADDEG_CY | HAGEBASECY | HHPOP_CY | HINC0_CY | HINC150_CY | HINC15_CY | HINC25_CY | HINC35_CY | HINC50_CY | HINC75_CY | HINCBASECY | HISPAI_CY | HISPASN_CY | HISPBLK_CY | HISPMLT_CY | HISPOTH_CY | HISPPI_CY | HISPPOP_CY | HISPWHT_CY | HSGRAD_CY | HSPFBASECY | HSPMBASECY | IAGEBASECY | LANDAREA | MAL18UP_CY | MAL21UP_CY | MALE0_CY | MALE15_CY | MALE20_CY | MALE25_CY | MALE30_CY | MALE35_CY | MALE40_CY | MALE45_CY | MALE50_CY | MALE55_CY | MALE5_CY | MALE60_CY | MALE65_CY | MALE70_CY | MALE75_CY | MALE80_CY | MALE85_CY | MALES_CY | MARBASE_CY | MARRIED_CY | MEDAGE_CY | MEDDI_CY | MEDFAGE_CY | MEDHHR_CY | MEDHINC_CY | MEDMAGE_CY | MEDNW_CY | MEDVAL_CY | MILLENN_CY | MINORITYCY | MLTFBASECY | MLTMBASECY | NEVMARR_CY | NHSPAI_CY | NHSPASN_CY | NHSPBLK_CY | NHSPMLT_CY | NHSPOTH_CY | NHSPPI_CY | NHSPWHT_CY | NOHS_CY | NONHISP_CY | OAGEBASECY | OLDRGENSCY | OTHFBASECY | OTHMBASECY | OTHRACE_CY | OWNER_CY | PACIFIC_CY | PAGEBASECY | PCI_CY | PIFBASE_CY | PIMBASE_CY | POP0_CY | POP15_CY | POP18UP_CY | POP20_CY | POP21UP_CY | POP25_CY | POP30_CY | POP35_CY | POP40_CY | POP45_CY | POP50_CY | POP55_CY | POP5_CY | POP60_CY | POP65_CY | POP70_CY | POP75_CY | POP80_CY | POP85_CY | POPDENS_CY | RACE2UP_CY | RACEBASECY | RENTER_CY | SMCOLL_CY | SOMEHS_CY | TOTHH_CY | TOTHU_CY | TOTPOP_CY | UNEMPRT_CY | UNEMP_CY | VACANT_CY | WAGEBASECY | WHITE_CY | WHTFBASECY | WHTMBASECY | WIDOWED_CY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | CA | City and County of San Francisco | 330.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 30 | CA | Sacramento | 329.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 35 | MD | City of Baltimore | 298.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 39 | HI | City and County of Honolulu | 286.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 50 | CO | City and County of Denver | 251.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
null_data.shape
(125, 174)
# Rows where all columns (except region, subregion, Provider Count) are null
test_newcounty_df.index[test_newcounty_df.iloc[:,3:].isnull().all(1)]
Int64Index([ 29, 30, 35, 39, 50, 70, 78, 128, 132, 157,
...
1244, 1245, 1251, 1254, 1259, 1266, 1268, 1271, 1278, 1280],
dtype='int64', length=125)
test_newcounty_df.dropna(inplace=True)
test_newcounty_df.isnull().sum().sum()
0
# Check if any values are 0 in the dataframe
# test_newstate_df.isnull().values.any()
test_newcounty_df.columns[test_newcounty_df.eq(0).any()].tolist()
['GQPOP_CY', 'HISPAI_CY', 'HISPASN_CY', 'HISPBLK_CY', 'HISPPI_CY', 'NHSPOTH_CY', 'NHSPPI_CY', 'OTHMBASECY', 'PACIFIC_CY', 'PAGEBASECY', 'PIFBASE_CY', 'PIMBASE_CY']
test_newcounty_df.drop(['GQPOP_CY','HISPAI_CY','HISPASN_CY','HISPBLK_CY','HISPPI_CY','NHSPOTH_CY','NHSPPI_CY','OTHMBASECY','PACIFIC_CY','PAGEBASECY','PIFBASE_CY','PIMBASE_CY'], axis=1, inplace=True)
test_newcounty_df.head()
| regionabbr | subregion | Provider_Count | AAGEBASECY | AGGDI_CY | AGGHINC_CY | AGGINC_CY | AGGNW_CY | AIFBASE_CY | AIMBASE_CY | AMERIND_CY | AREA | ASIAN_CY | ASNFBASECY | ASNMBASECY | ASSCDEG_CY | AVGDI_CY | AVGFMSZ_CY | AVGHHSZ_CY | AVGHINC_CY | AVGNW_CY | AVGVAL_CY | BABYBOOMCY | BACHDEG_CY | BAGEBASECY | BLACK_CY | BLKFBASECY | BLKMBASECY | CIVLBFR_CY | EDUCBASECY | EMP_CY | FAMHH_CY | FAMPOP_CY | FEM0_CY | FEM15_CY | FEM18UP_CY | FEM20_CY | FEM21UP_CY | FEM25_CY | FEM30_CY | FEM35_CY | FEM40_CY | FEM45_CY | FEM50_CY | FEM55_CY | FEM5_CY | FEM60_CY | FEM65_CY | FEM70_CY | FEM75_CY | FEM80_CY | FEM85_CY | FEMALES_CY | GED_CY | GENALPHACY | GENBASE_CY | GENX_CY | GENZ_CY | GRADDEG_CY | HAGEBASECY | HHPOP_CY | HINC0_CY | HINC150_CY | HINC15_CY | HINC25_CY | HINC35_CY | HINC50_CY | HINC75_CY | HINCBASECY | HISPMLT_CY | HISPOTH_CY | HISPPOP_CY | HISPWHT_CY | HSGRAD_CY | HSPFBASECY | HSPMBASECY | IAGEBASECY | LANDAREA | MAL18UP_CY | MAL21UP_CY | MALE0_CY | MALE15_CY | MALE20_CY | MALE25_CY | MALE30_CY | MALE35_CY | MALE40_CY | MALE45_CY | MALE50_CY | MALE55_CY | MALE5_CY | MALE60_CY | MALE65_CY | MALE70_CY | MALE75_CY | MALE80_CY | MALE85_CY | MALES_CY | MARBASE_CY | MARRIED_CY | MEDAGE_CY | MEDDI_CY | MEDFAGE_CY | MEDHHR_CY | MEDHINC_CY | MEDMAGE_CY | MEDNW_CY | MEDVAL_CY | MILLENN_CY | MINORITYCY | MLTFBASECY | MLTMBASECY | NEVMARR_CY | NHSPAI_CY | NHSPASN_CY | NHSPBLK_CY | NHSPMLT_CY | NHSPWHT_CY | NOHS_CY | NONHISP_CY | OAGEBASECY | OLDRGENSCY | OTHFBASECY | OTHRACE_CY | OWNER_CY | PCI_CY | POP0_CY | POP15_CY | POP18UP_CY | POP20_CY | POP21UP_CY | POP25_CY | POP30_CY | POP35_CY | POP40_CY | POP45_CY | POP50_CY | POP55_CY | POP5_CY | POP60_CY | POP65_CY | POP70_CY | POP75_CY | POP80_CY | POP85_CY | POPDENS_CY | RACE2UP_CY | RACEBASECY | RENTER_CY | SMCOLL_CY | SOMEHS_CY | TOTHH_CY | TOTHU_CY | TOTPOP_CY | UNEMPRT_CY | UNEMP_CY | VACANT_CY | WAGEBASECY | WHITE_CY | WHTFBASECY | WHTMBASECY | WIDOWED_CY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CA | Los Angeles County | 2094.0 | 1517296.0 | 2.359152e+11 | 3.196410e+11 | 3.247511e+11 | 2.224411e+12 | 35957.0 | 37602.0 | 73559.0 | 4085.699440 | 1517296.0 | 805865.0 | 711431.0 | 469649.0 | 70013.0 | 3.63 | 3.00 | 94861.0 | 660145.0 | 710686.0 | 1987624.0 | 1445895.0 | 850350.0 | 850350.0 | 450243.0 | 400107.0 | 5232399.0 | 6899087.0 | 4995839.0 | 2281483.0 | 8292844.0 | 312953.0 | 331081.0 | 4077284.0 | 384255.0 | 3858162.0 | 421330.0 | 392960.0 | 359612.0 | 328513.0 | 335961.0 | 332084.0 | 327978.0 | 310887.0 | 291733.0 | 246493.0 | 183490.0 | 131256.0 | 90178.0 | 110499.0 | 5208957.0 | 120351.0 | 255399.0 | 10288937.0 | 2130083.0 | 2353920.0 | 782758.0 | 5043293.0 | 10112518.0 | 371190.0 | 243110.0 | 310104.0 | 280437.0 | 390334.0 | 554983.0 | 394431.0 | 3369582.0 | 261906.0 | 2280356.0 | 5043293.0 | 2374599.0 | 1288396.0 | 2522627.0 | 2520666.0 | 73559.0 | 4057.8835 | 3890494.0 | 3663128.0 | 326230.0 | 351424.0 | 396276.0 | 433863.0 | 402148.0 | 368194.0 | 325765.0 | 331204.0 | 323486.0 | 307396.0 | 325231.0 | 261725.0 | 209710.0 | 149559.0 | 102402.0 | 65741.0 | 65807.0 | 5079980.0 | 8362123.0 | 3752436.0 | 35.7 | 52454.0 | 36.9 | 50.6 | 62751.0 | 34.6 | 60024.0 | 569360.0 | 2875966.0 | 7644196.0 | 251940.0 | 244185.0 | 3521307.0 | 18790.0 | 1494472.0 | 805337.0 | 234219.0 | 2644741.0 | 878838.0 | 5245644.0 | 2305030.0 | 685945.0 | 1141774.0 | 2305030.0 | 1538585.0 | 31563.0 | 639183.0 | 682505.0 | 7967778.0 | 780531.0 | 7521290.0 | 855193.0 | 795108.0 | 727806.0 | 654278.0 | 667165.0 | 655570.0 | 635374.0 | 636118.0 | 553458.0 | 456203.0 | 333049.0 | 233658.0 | 155919.0 | 176306.0 | 2535.5 | 496125.0 | 10288937.0 | 1831065.0 | 1300786.0 | 612414.0 | 3369650.0 | 3576478.0 | 10288937.0 | 4.5 | 236560.0 | 206828.0 | 5019340.0 | 5019340.0 | 2509432.0 | 2509908.0 | 413837.0 |
| 1 | IL | Cook County | 1586.0 | 395712.0 | 1.284204e+11 | 1.771502e+11 | 1.797564e+11 | 1.326345e+12 | 10418.0 | 10695.0 | 21113.0 | 953.145682 | 395712.0 | 206041.0 | 189671.0 | 237028.0 | 63978.0 | 3.36 | 2.58 | 88254.0 | 660769.0 | 335311.0 | 1076607.0 | 830433.0 | 1233716.0 | 1233716.0 | 674544.0 | 559172.0 | 2791109.0 | 3607345.0 | 2599303.0 | 1216270.0 | 4087547.0 | 158205.0 | 161652.0 | 2144742.0 | 185272.0 | 2040962.0 | 217698.0 | 207243.0 | 188312.0 | 166222.0 | 164629.0 | 166570.0 | 174115.0 | 159117.0 | 162326.0 | 139839.0 | 104629.0 | 75554.0 | 54048.0 | 70522.0 | 2717277.0 | 95959.0 | 129327.0 | 5274129.0 | 1058747.0 | 1171989.0 | 552050.0 | 1374256.0 | 5183187.0 | 250519.0 | 132865.0 | 186804.0 | 169815.0 | 238778.0 | 329118.0 | 243670.0 | 2007274.0 | 66348.0 | 601231.0 | 1374256.0 | 664024.0 | 726645.0 | 671160.0 | 703096.0 | 21113.0 | 945.3262 | 1963911.0 | 1860397.0 | 163128.0 | 166591.0 | 180196.0 | 214748.0 | 206465.0 | 188502.0 | 162787.0 | 160501.0 | 157805.0 | 158061.0 | 164098.0 | 141495.0 | 116039.0 | 82196.0 | 55154.0 | 35924.0 | 35961.0 | 2556852.0 | 4301056.0 | 1851961.0 | 36.6 | 47971.0 | 37.9 | 50.8 | 59718.0 | 35.4 | 76719.0 | 256912.0 | 1442885.0 | 3078944.0 | 78362.0 | 75557.0 | 1813320.0 | 5982.0 | 391629.0 | 1210983.0 | 87571.0 | 2195185.0 | 237082.0 | 3899873.0 | 608672.0 | 394574.0 | 294742.0 | 608672.0 | 1108536.0 | 34083.0 | 321333.0 | 328243.0 | 4108653.0 | 365468.0 | 3901359.0 | 432446.0 | 413708.0 | 376814.0 | 329009.0 | 325130.0 | 324375.0 | 332176.0 | 323215.0 | 303821.0 | 255878.0 | 186825.0 | 130708.0 | 89972.0 | 106483.0 | 5579.2 | 153919.0 | 5274129.0 | 898740.0 | 690225.0 | 237923.0 | 2007276.0 | 2230295.0 | 5274129.0 | 6.9 | 191806.0 | 223019.0 | 2859209.0 | 2859209.0 | 1452300.0 | 1406909.0 | 250105.0 |
| 2 | NY | New York County | 1136.0 | 212844.0 | 6.672674e+10 | 1.091191e+11 | 1.109282e+11 | 4.008776e+11 | 4542.0 | 4214.0 | 8756.0 | 22.950686 | 212844.0 | 119067.0 | 93777.0 | 48360.0 | 83690.0 | 3.02 | 2.00 | 136860.0 | 502791.0 | 1307852.0 | 345825.0 | 406878.0 | 247085.0 | 247085.0 | 133471.0 | 113614.0 | 996167.0 | 1251653.0 | 956908.0 | 314952.0 | 951099.0 | 35672.0 | 40691.0 | 760316.0 | 71461.0 | 725473.0 | 95217.0 | 86134.0 | 68890.0 | 53980.0 | 50374.0 | 50312.0 | 53400.0 | 32553.0 | 52503.0 | 49261.0 | 38526.0 | 27262.0 | 18025.0 | 22645.0 | 878322.0 | 26610.0 | 30855.0 | 1660472.0 | 339808.0 | 248875.0 | 371255.0 | 441304.0 | 1593746.0 | 107793.0 | 70466.0 | 58555.0 | 49858.0 | 60209.0 | 94133.0 | 78323.0 | 797304.0 | 36560.0 | 187987.0 | 441304.0 | 163702.0 | 125099.0 | 233374.0 | 207930.0 | 8756.0 | 22.8287 | 660480.0 | 632378.0 | 37014.0 | 36186.0 | 57628.0 | 82593.0 | 79654.0 | 67405.0 | 53997.0 | 51176.0 | 48844.0 | 48003.0 | 33820.0 | 43057.0 | 37266.0 | 28231.0 | 20234.0 | 12716.0 | 11948.0 | 782150.0 | 1457619.0 | 517739.0 | 37.9 | 61201.0 | 38.3 | 48.2 | 82611.0 | 37.4 | 47793.0 | 1216203.0 | 557913.0 | 899778.0 | 40103.0 | 33176.0 | 745585.0 | 2164.0 | 210747.0 | 203134.0 | 36719.0 | 760694.0 | 80622.0 | 1219168.0 | 193191.0 | 137196.0 | 101593.0 | 193191.0 | 185125.0 | 66805.0 | 72686.0 | 76877.0 | 1420796.0 | 129089.0 | 1357851.0 | 177810.0 | 165788.0 | 136295.0 | 107977.0 | 101550.0 | 99156.0 | 101403.0 | 66373.0 | 95560.0 | 86527.0 | 66757.0 | 47496.0 | 30741.0 | 34593.0 | 72736.2 | 73279.0 | 1660472.0 | 612187.0 | 121070.0 | 71759.0 | 797312.0 | 890327.0 | 1660472.0 | 3.9 | 39259.0 | 93015.0 | 924396.0 | 924396.0 | 479062.0 | 445334.0 | 68402.0 |
| 3 | TX | Harris County | 1124.0 | 341640.0 | 1.132993e+11 | 1.470200e+11 | 1.487312e+11 | 1.000460e+12 | 14364.0 | 15637.0 | 30001.0 | 1748.811066 | 341640.0 | 173439.0 | 168201.0 | 193511.0 | 68781.0 | 3.49 | 2.85 | 89252.0 | 607353.0 | 251600.0 | 848003.0 | 605813.0 | 901459.0 | 901459.0 | 478023.0 | 423436.0 | 2335018.0 | 3029538.0 | 2196764.0 | 1124808.0 | 3920902.0 | 176809.0 | 152219.0 | 1773341.0 | 169057.0 | 1680385.0 | 196592.0 | 183051.0 | 171499.0 | 154056.0 | 148493.0 | 141554.0 | 141858.0 | 172608.0 | 125458.0 | 102481.0 | 70111.0 | 46215.0 | 29690.0 | 32228.0 | 2380573.0 | 98795.0 | 145111.0 | 4735852.0 | 961416.0 | 1219972.0 | 345886.0 | 2035551.0 | 4686760.0 | 161383.0 | 106787.0 | 153424.0 | 159410.0 | 212729.0 | 290829.0 | 190407.0 | 1647247.0 | 100351.0 | 703910.0 | 2035551.0 | 1177199.0 | 608836.0 | 994167.0 | 1041384.0 | 30001.0 | 1703.4776 | 1723465.0 | 1625451.0 | 182838.0 | 161982.0 | 172174.0 | 206139.0 | 192088.0 | 176799.0 | 153424.0 | 146647.0 | 137454.0 | 132895.0 | 178822.0 | 114553.0 | 90812.0 | 60215.0 | 36665.0 | 20887.0 | 17674.0 | 2355279.0 | 3684970.0 | 1809576.0 | 33.5 | 50339.0 | 34.3 | 47.5 | 59417.0 | 32.7 | 72820.0 | 176533.0 | 1332331.0 | 3339578.0 | 85910.0 | 87501.0 | 1363826.0 | 8650.0 | 337748.0 | 873461.0 | 73060.0 | 1396274.0 | 316268.0 | 2700301.0 | 712410.0 | 229019.0 | 342097.0 | 712410.0 | 888580.0 | 31405.0 | 359647.0 | 314201.0 | 3496806.0 | 341231.0 | 3305836.0 | 402731.0 | 375139.0 | 348298.0 | 307480.0 | 295140.0 | 279008.0 | 274753.0 | 351430.0 | 240011.0 | 193293.0 | 130326.0 | 82880.0 | 50577.0 | 49902.0 | 2780.1 | 173411.0 | 4735852.0 | 758671.0 | 606842.0 | 253587.0 | 1647251.0 | 1790697.0 | 4735852.0 | 5.9 | 138254.0 | 143446.0 | 2573473.0 | 2573473.0 | 1284972.0 | 1288501.0 | 161546.0 |
| 4 | AZ | Maricopa County | 844.0 | 184150.0 | 1.043304e+11 | 1.333034e+11 | 1.346919e+11 | 1.310493e+12 | 51929.0 | 46655.0 | 98584.0 | 9224.040205 | 184150.0 | 97892.0 | 86258.0 | 241714.0 | 64964.0 | 3.32 | 2.69 | 83005.0 | 816017.0 | 303705.0 | 872897.0 | 597653.0 | 253576.0 | 253576.0 | 124641.0 | 128935.0 | 2181523.0 | 2891837.0 | 2065771.0 | 1053223.0 | 3492063.0 | 149041.0 | 138470.0 | 1694648.0 | 151115.0 | 1606050.0 | 165009.0 | 153091.0 | 145864.0 | 134160.0 | 133211.0 | 130665.0 | 133428.0 | 147853.0 | 125740.0 | 117630.0 | 90474.0 | 62955.0 | 42348.0 | 51162.0 | 2217638.0 | 95969.0 | 119899.0 | 4387226.0 | 855726.0 | 1066649.0 | 342159.0 | 1373153.0 | 4322686.0 | 163449.0 | 98771.0 | 135427.0 | 144542.0 | 215958.0 | 297023.0 | 213940.0 | 1605964.0 | 73969.0 | 592016.0 | 1373153.0 | 660605.0 | 548954.0 | 682683.0 | 690470.0 | 98584.0 | 9200.1431 | 1624795.0 | 1531223.0 | 154750.0 | 146575.0 | 157726.0 | 172645.0 | 157924.0 | 148689.0 | 134272.0 | 132380.0 | 127236.0 | 123794.0 | 153320.0 | 111456.0 | 101867.0 | 78340.0 | 52720.0 | 33351.0 | 31426.0 | 2169588.0 | 3485723.0 | 1676208.0 | 35.8 | 50661.0 | 37.0 | 50.4 | 59691.0 | 34.7 | 102641.0 | 237947.0 | 1136843.0 | 1981147.0 | 89351.0 | 87304.0 | 1200935.0 | 74636.0 | 179434.0 | 236991.0 | 102686.0 | 2406079.0 | 160973.0 | 3014073.0 | 598035.0 | 335212.0 | 292917.0 | 598035.0 | 998231.0 | 30701.0 | 303791.0 | 285045.0 | 3319443.0 | 308841.0 | 3137273.0 | 337654.0 | 311015.0 | 294553.0 | 268432.0 | 265591.0 | 257901.0 | 257222.0 | 301173.0 | 237196.0 | 219497.0 | 168814.0 | 115675.0 | 75699.0 | 82588.0 | 476.9 | 176655.0 | 4387226.0 | 607760.0 | 711033.0 | 193382.0 | 1605991.0 | 1813056.0 | 4387226.0 | 5.3 | 115752.0 | 207065.0 | 3066684.0 | 3066684.0 | 1556262.0 | 1510422.0 | 182502.0 |
test_newcounty_df.columns[test_newcounty_df.eq(0).any()].tolist()
[]
test_newcounty_df['MEDHINC_CY'] = test_newcounty_df['MEDHINC_CY'].astype(float)
type(test_newcounty_df['MEDHINC_CY'][0])
numpy.float64
# Transform Data
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import PowerTransformer
# pt_county = PowerTransformer(method='box-cox')
sc_county = StandardScaler()
newcounty_df_pt = sc_county.fit_transform(test_newcounty_df.iloc[:,2:])
from sklearn.cluster import KMeans
sns.set(rc={'figure.figsize':(15,8)})
distortions = []
for i in range(1,20):
km = KMeans(n_clusters=i,
init='k-means++',
n_init=10,
max_iter=300,
random_state=0)
km.fit(newcounty_df_pt)
distortions.append(km.inertia_)
plt.plot(range(1,20), distortions, marker='o')
plt.xlabel('No. of Clusters')
plt.ylabel('Within Cluster Sum of Squares')
plt.show()
sns.set(rc={'figure.figsize':(15,8)})
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=4)
# Fit the data
kmeans.fit(newcounty_df_pt)
# Get the cluster labels
y_pred = kmeans.predict(newcounty_df_pt)
# plot the cluster assignments and cluster centers
# newstate_df_std[:,0] includes all values for Provider Count and so on... #103 is MEDAGE_CY
plt.scatter(newcounty_df_pt[:, 0], newcounty_df_pt[:,103], c=y_pred, cmap="plasma")
plt.scatter(kmeans.cluster_centers_[:, 0],
kmeans.cluster_centers_[:, 103],
marker='^',
c=[0,1,2,3],
s=100,
linewidth=2,
cmap="plasma")
plt.xlabel("Feature 0")
plt.ylabel("Feature 1")
Text(0, 0.5, 'Feature 1')
kmeans.cluster_centers_
array([[-2.29317364e-01, -1.64233279e-01, -2.43219407e-01,
-2.42675763e-01, -2.42411870e-01, -2.58458545e-01,
-1.25736786e-01, -1.29842473e-01, -1.27814032e-01,
-6.32433407e-03, -1.64233279e-01, -1.63502697e-01,
-1.64971132e-01, -2.43841359e-01, -1.71135994e-01,
-1.09379498e-01, -7.08485772e-02, -1.78882907e-01,
-1.20255165e-01, -1.61501958e-01, -2.42271740e-01,
-2.39829177e-01, -2.01811386e-01, -2.01811386e-01,
-1.99028814e-01, -2.04901056e-01, -2.35039704e-01,
-2.34328504e-01, -2.35124412e-01, -2.39545162e-01,
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1.11789665e+00, 1.02258402e+00, 1.12991744e+00,
1.14105604e+00, 1.13678940e+00, 1.12423365e+00,
1.13778017e+00, 1.17096119e+00, 4.58585659e-01,
8.88483719e-01, 1.03610082e+00, 8.49491389e-01,
1.04662187e+00, 7.46217812e-01, 1.10472706e+00,
1.10180454e+00, 1.03610082e+00, -2.70384574e-02,
9.88347940e-01, 9.42059826e-01, 1.20470734e+00,
1.20470734e+00, 1.21780852e+00, 1.19108396e+00,
1.08633317e+00]])
# Create dataframe of Cluster labels and State Names
clusterdf = pd.DataFrame(kmeans.labels_, test_newcounty_df['subregion'])
clusterdf.reset_index(inplace=True)
clusterdf.columns = ['County','Cluster']
clusterdf.head()
| County | Cluster | |
|---|---|---|
| 0 | Los Angeles County | 2 |
| 1 | Cook County | 1 |
| 2 | New York County | 1 |
| 3 | Harris County | 1 |
| 4 | Maricopa County | 1 |
# Count of counties in each cluster
clusterdf['Cluster'].value_counts()
0 1015 3 123 1 18 2 1 Name: Cluster, dtype: int64
# Counties in each cluster
clusterdf.groupby('Cluster')['County'].apply(list)
Cluster 0 [Lehigh County, Washtenaw County, Pulaski Coun... 1 [Cook County, New York County, Harris County, ... 2 [Los Angeles County] 3 [Philadelphia County, Oakland County, Fulton C... Name: County, dtype: object
# Get provider data for obgyn providers only
obgyn_count_df.head()
| regionabbr | Provider_Count | |
|---|---|---|
| 0 | CA | 7543 |
| 1 | TX | 5161 |
| 2 | NY | 5054 |
| 3 | FL | 4170 |
| 4 | PA | 3283 |
allstate_featureset = state_layer.query()
allstate_df = allstate_featureset.sdf
allstate_df.head()
Error invoking service Error generating token
--------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-144-a9c8dabe6cbf> in <module> ----> 1 allstate_featureset = state_layer.query() 2 allstate_df = allstate_featureset.sdf 3 allstate_df.head() ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\features\layer.py in query(self, where, out_fields, time_filter, geometry_filter, return_geometry, return_count_only, return_ids_only, return_distinct_values, return_extent_only, group_by_fields_for_statistics, statistic_filter, result_offset, result_record_count, object_ids, distance, units, max_allowable_offset, out_sr, geometry_precision, gdb_version, order_by_fields, out_statistics, return_z, return_m, multipatch_option, quantization_parameters, return_centroid, return_all_records, result_type, historic_moment, sql_format, return_true_curves, return_exceeded_limit_features, as_df, **kwargs) 698 699 params['returnCountOnly'] = True --> 700 record_count = self._query(url, params, raw=as_raw) 701 if 'maxRecordCount' in self.properties: 702 max_records = self.properties['maxRecordCount'] ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\features\layer.py in _query(self, url, params, raw) 1397 """ returns results of query """ 1398 result = self._con.post(path=url, -> 1399 postdata=params, token=self._token) 1400 if 'error' in result: 1401 raise ValueError(result) ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\connection.py in post(self, path, postdata, files, ssl, compress, is_retry, use_ordered_dict, add_token, verify_cert, token, try_json, out_folder, file_name, force_bytes, add_headers) 1175 verify_cert=verify_cert, is_retry=True) 1176 -> 1177 self._handle_json_error(resp_json['error'], errorcode) 1178 return None 1179 ~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\connection.py in _handle_json_error(self, error, errorcode) 1196 1197 errormessage = errormessage + "\n(Error Code: " + str(errorcode) +")" -> 1198 raise RuntimeError(errormessage) 1199 1200 class _StrictURLopener(request.FancyURLopener): RuntimeError: Error invoking service Error generating token (Error Code: 500)
for field in state_layer.properties.fields:
# print(field)
print(field['name'], '\t', field['alias'])
# print(field[0])
OBJECTID OBJECTID Shape Shape ID ID NAME NAME STATE_NAME STATE_NAME ST_ABBREV ST_ABBREV AREA Area in Square Miles (Calculated) TOTPOP_CY 2018 Total Population (Esri) HHPOP_CY 2018 Household Population (Esri) FAMPOP_CY 2018 Family Population (Esri) GQPOP_CY 2018 Group Quarters Population (Esri) POPDENS_CY 2018 Population Density (Pop per Square Mile) (Esri) TOTHH_CY 2018 Total Households (Esri) AVGHHSZ_CY 2018 Average Household Size (Esri) FAMHH_CY 2018 Total Family Households (Esri) AVGFMSZ_CY 2018 Average Family Size (Esri) TOTHU_CY 2018 Total Housing Units (Esri) OWNER_CY 2018 Owner Occupied Housing Units (Esri) RENTER_CY 2018 Renter Occupied Housing Units (Esri) VACANT_CY 2018 Vacant Housing Units (Esri) POPGRW10CY 2010-2018 Population: Annual Growth Rate (Esri) HHGRW10CY 2010-2018 Households: Annual Growth Rate (Esri) FAMGRW10CY 2010-2018 Families: Annual Growth Rate (Esri) GENALPHACY 2018 Generation Alpha Population (Born 2017 or Later) GENZ_CY 2018 Generation Z Population (Born 1999 to 2016) MILLENN_CY 2018 Millennial Population (Born 1981 to 1998) GENX_CY 2018 Generation X Population (Born 1965 to 1980) BABYBOOMCY 2018 Baby Boomer Population (Born 1946 to 1964) OLDRGENSCY 2018 Silent & Greatest Generations Population (Born 1945/Earlier) GENBASE_CY 2018 Population by Generation Base POP0_CY 2018 Total Population Age 0-4 (Esri) POP5_CY 2018 Total Population Age 5-9 (Esri) POP10_CY 2018 Total Population Age 10-14 (Esri) POP15_CY 2018 Total Population Age 15-19 (Esri) POP20_CY 2018 Total Population Age 20-24 (Esri) POP25_CY 2018 Total Population Age 25-29 (Esri) POP30_CY 2018 Total Population Age 30-34 (Esri) POP35_CY 2018 Total Population Age 35-39 (Esri) POP40_CY 2018 Total Population Age 40-44 (Esri) POP45_CY 2018 Total Population Age 45-49 (Esri) POP50_CY 2018 Total Population Age 50-54 (Esri) POP55_CY 2018 Total Population Age 55-59 (Esri) POP60_CY 2018 Total Population Age 60-64 (Esri) POP65_CY 2018 Total Population Age 65-69 (Esri) POP70_CY 2018 Total Population Age 70-74 (Esri) POP75_CY 2018 Total Population Age 75-79 (Esri) POP80_CY 2018 Total Population Age 80-84 (Esri) POP85_CY 2018 Total Population Age 85+ (Esri) POP18UP_CY 2018 Total Population Age 18+ (Esri) POP21UP_CY 2018 Total Population Age 21+ (Esri) MEDAGE_CY 2018 Median Age (Esri) MALES_CY 2018 Male Population (Esri) MALE0_CY 2018 Male Population Age 0-4 (Esri) MALE5_CY 2018 Male Population Age 5-9 (Esri) MALE10_CY 2018 Male Population Age 10-14 (Esri) MALE15_CY 2018 Male Population Age 15-19 (Esri) MALE20_CY 2018 Male Population Age 20-24 (Esri) MALE25_CY 2018 Male Population Age 25-29 (Esri) MALE30_CY 2018 Male Population Age 30-34 (Esri) MALE35_CY 2018 Male Population Age 35-39 (Esri) MALE40_CY 2018 Male Population Age 40-44 (Esri) MALE45_CY 2018 Male Population Age 45-49 (Esri) MALE50_CY 2018 Male Population Age 50-54 (Esri) MALE55_CY 2018 Male Population Age 55-59 (Esri) MALE60_CY 2018 Male Population Age 60-64 (Esri) MALE65_CY 2018 Male Population Age 65-69 (Esri) MALE70_CY 2018 Male Population Age 70-74 (Esri) MALE75_CY 2018 Male Population Age 75-79 (Esri) MALE80_CY 2018 Male Population Age 80-84 (Esri) MALE85_CY 2018 Male Population Age 85+ (Esri) MAL18UP_CY 2018 Male Population Age 18+ (Esri) MAL21UP_CY 2018 Male Population Age 21+ (Esri) MEDMAGE_CY 2018 Median Male Age (Esri) FEMALES_CY 2018 Female Population (Esri) FEM0_CY 2018 Female Population Age 0-4 (Esri) FEM5_CY 2018 Female Population Age 5-9 (Esri) FEM10_CY 2018 Female Population Age 10-14 (Esri) FEM15_CY 2018 Female Population Age 15-19 (Esri) FEM20_CY 2018 Female Population Age 20-24 (Esri) FEM25_CY 2018 Female Population Age 25-29 (Esri) FEM30_CY 2018 Female Population Age 30-34 (Esri) FEM35_CY 2018 Female Population Age 35-39 (Esri) FEM40_CY 2018 Female Population Age 40-44 (Esri) FEM45_CY 2018 Female Population Age 45-49 (Esri) FEM50_CY 2018 Female Population Age 50-54 (Esri) FEM55_CY 2018 Female Population Age 55-59 (Esri) FEM60_CY 2018 Female Population Age 60-64 (Esri) FEM65_CY 2018 Female Population Age 65-69 (Esri) FEM70_CY 2018 Female Population Age 70-74 (Esri) FEM75_CY 2018 Female Population Age 75-79 (Esri) FEM80_CY 2018 Female Population Age 80-84 (Esri) FEM85_CY 2018 Female Population Age 85+ (Esri) FEM18UP_CY 2018 Female Population Age 18+ (Esri) FEM21UP_CY 2018 Female Population Age 21+ (Esri) MEDFAGE_CY 2018 Median Female Age (Esri) AGEBASE_CY 2018 Total Population by Five-Year Age Base (Esri) AGE0_CY 2018 Total Population Age <1 (Esri) AGE1_CY 2018 Total Population Age 1 (Esri) AGE2_CY 2018 Total Population Age 2 (Esri) AGE3_CY 2018 Total Population Age 3 (Esri) AGE4_CY 2018 Total Population Age 4 (Esri) AGE5_CY 2018 Total Population Age 5 (Esri) AGE6_CY 2018 Total Population Age 6 (Esri) AGE7_CY 2018 Total Population Age 7 (Esri) AGE8_CY 2018 Total Population Age 8 (Esri) AGE9_CY 2018 Total Population Age 9 (Esri) AGE10_CY 2018 Total Population Age 10 (Esri) AGE11_CY 2018 Total Population Age 11 (Esri) AGE12_CY 2018 Total Population Age 12 (Esri) AGE13_CY 2018 Total Population Age 13 (Esri) AGE14_CY 2018 Total Population Age 14 (Esri) AGE15_CY 2018 Total Population Age 15 (Esri) AGE16_CY 2018 Total Population Age 16 (Esri) AGE17_CY 2018 Total Population Age 17 (Esri) AGE18_CY 2018 Total Population Age 18 (Esri) AGE19_CY 2018 Total Population Age 19 (Esri) AGE20_CY 2018 Total Population Age 20 (Esri) AGE21_CY 2018 Total Population Age 21 (Esri) AGE22_CY 2018 Total Population Age 22 (Esri) AGE23_CY 2018 Total Population Age 23 (Esri) AGE24_CY 2018 Total Population Age 24 (Esri) AGE25_CY 2018 Total Population Age 25 (Esri) AGE26_CY 2018 Total Population Age 26 (Esri) AGE27_CY 2018 Total Population Age 27 (Esri) AGE28_CY 2018 Total Population Age 28 (Esri) AGE29_CY 2018 Total Population Age 29 (Esri) AGE30_CY 2018 Total Population Age 30 (Esri) AGE31_CY 2018 Total Population Age 31 (Esri) AGE32_CY 2018 Total Population Age 32 (Esri) AGE33_CY 2018 Total Population Age 33 (Esri) AGE34_CY 2018 Total Population Age 34 (Esri) AGE35_CY 2018 Total Population Age 35 (Esri) AGE36_CY 2018 Total Population Age 36 (Esri) AGE37_CY 2018 Total Population Age 37 (Esri) AGE38_CY 2018 Total Population Age 38 (Esri) AGE39_CY 2018 Total Population Age 39 (Esri) AGE40_CY 2018 Total Population Age 40 (Esri) AGE41_CY 2018 Total Population Age 41 (Esri) AGE42_CY 2018 Total Population Age 42 (Esri) AGE43_CY 2018 Total Population Age 43 (Esri) AGE44_CY 2018 Total Population Age 44 (Esri) AGE45_CY 2018 Total Population Age 45 (Esri) AGE46_CY 2018 Total Population Age 46 (Esri) AGE47_CY 2018 Total Population Age 47 (Esri) AGE48_CY 2018 Total Population Age 48 (Esri) AGE49_CY 2018 Total Population Age 49 (Esri) AGE50_CY 2018 Total Population Age 50 (Esri) AGE51_CY 2018 Total Population Age 51 (Esri) AGE52_CY 2018 Total Population Age 52 (Esri) AGE53_CY 2018 Total Population Age 53 (Esri) AGE54_CY 2018 Total Population Age 54 (Esri) AGE55_CY 2018 Total Population Age 55 (Esri) AGE56_CY 2018 Total Population Age 56 (Esri) AGE57_CY 2018 Total Population Age 57 (Esri) AGE58_CY 2018 Total Population Age 58 (Esri) AGE59_CY 2018 Total Population Age 59 (Esri) AGE60_CY 2018 Total Population Age 60 (Esri) AGE61_CY 2018 Total Population Age 61 (Esri) AGE62_CY 2018 Total Population Age 62 (Esri) AGE63_CY 2018 Total Population Age 63 (Esri) AGE64_CY 2018 Total Population Age 64 (Esri) AGE65_CY 2018 Total Population Age 65 (Esri) AGE66_CY 2018 Total Population Age 66 (Esri) AGE67_CY 2018 Total Population Age 67 (Esri) AGE68_CY 2018 Total Population Age 68 (Esri) AGE69_CY 2018 Total Population Age 69 (Esri) AGE70_CY 2018 Total Population Age 70 (Esri) AGE71_CY 2018 Total Population Age 71 (Esri) AGE72_CY 2018 Total Population Age 72 (Esri) AGE73_CY 2018 Total Population Age 73 (Esri) AGE74_CY 2018 Total Population Age 74 (Esri) AGE75_CY 2018 Total Population Age 75 (Esri) AGE76_CY 2018 Total Population Age 76 (Esri) AGE77_CY 2018 Total Population Age 77 (Esri) AGE78_CY 2018 Total Population Age 78 (Esri) AGE79_CY 2018 Total Population Age 79 (Esri) AGE80_CY 2018 Total Population Age 80 (Esri) AGE81_CY 2018 Total Population Age 81 (Esri) AGE82_CY 2018 Total Population Age 82 (Esri) AGE83_CY 2018 Total Population Age 83 (Esri) AGE84_CY 2018 Total Population Age 84 (Esri) MAGE0_CY 2018 Male Population Age <1 (Esri) MAGE1_CY 2018 Male Population Age 1 (Esri) MAGE2_CY 2018 Male Population Age 2 (Esri) MAGE3_CY 2018 Male Population Age 3 (Esri) MAGE4_CY 2018 Male Population Age 4 (Esri) MAGE5_CY 2018 Male Population Age 5 (Esri) MAGE6_CY 2018 Male Population Age 6 (Esri) MAGE7_CY 2018 Male Population Age 7 (Esri) MAGE8_CY 2018 Male Population Age 8 (Esri) MAGE9_CY 2018 Male Population Age 9 (Esri) MAGE10_CY 2018 Male Population Age 10 (Esri) MAGE11_CY 2018 Male Population Age 11 (Esri) MAGE12_CY 2018 Male Population Age 12 (Esri) MAGE13_CY 2018 Male Population Age 13 (Esri) MAGE14_CY 2018 Male Population Age 14 (Esri) MAGE15_CY 2018 Male Population Age 15 (Esri) MAGE16_CY 2018 Male Population Age 16 (Esri) MAGE17_CY 2018 Male Population Age 17 (Esri) MAGE18_CY 2018 Male Population Age 18 (Esri) MAGE19_CY 2018 Male Population Age 19 (Esri) MAGE20_CY 2018 Male Population Age 20 (Esri) MAGE21_CY 2018 Male Population Age 21 (Esri) MAGE22_CY 2018 Male Population Age 22 (Esri) MAGE23_CY 2018 Male Population Age 23 (Esri) MAGE24_CY 2018 Male Population Age 24 (Esri) MAGE25_CY 2018 Male Population Age 25 (Esri) MAGE26_CY 2018 Male Population Age 26 (Esri) MAGE27_CY 2018 Male Population Age 27 (Esri) MAGE28_CY 2018 Male Population Age 28 (Esri) MAGE29_CY 2018 Male Population Age 29 (Esri) MAGE30_CY 2018 Male Population Age 30 (Esri) MAGE31_CY 2018 Male Population Age 31 (Esri) MAGE32_CY 2018 Male Population Age 32 (Esri) MAGE33_CY 2018 Male Population Age 33 (Esri) MAGE34_CY 2018 Male Population Age 34 (Esri) MAGE35_CY 2018 Male Population Age 35 (Esri) MAGE36_CY 2018 Male Population Age 36 (Esri) MAGE37_CY 2018 Male Population Age 37 (Esri) MAGE38_CY 2018 Male Population Age 38 (Esri) MAGE39_CY 2018 Male Population Age 39 (Esri) MAGE40_CY 2018 Male Population Age 40 (Esri) MAGE41_CY 2018 Male Population Age 41 (Esri) MAGE42_CY 2018 Male Population Age 42 (Esri) MAGE43_CY 2018 Male Population Age 43 (Esri) MAGE44_CY 2018 Male Population Age 44 (Esri) MAGE45_CY 2018 Male Population Age 45 (Esri) MAGE46_CY 2018 Male Population Age 46 (Esri) MAGE47_CY 2018 Male Population Age 47 (Esri) MAGE48_CY 2018 Male Population Age 48 (Esri) MAGE49_CY 2018 Male Population Age 49 (Esri) MAGE50_CY 2018 Male Population Age 50 (Esri) MAGE51_CY 2018 Male Population Age 51 (Esri) MAGE52_CY 2018 Male Population Age 52 (Esri) MAGE53_CY 2018 Male Population Age 53 (Esri) MAGE54_CY 2018 Male Population Age 54 (Esri) MAGE55_CY 2018 Male Population Age 55 (Esri) MAGE56_CY 2018 Male Population Age 56 (Esri) MAGE57_CY 2018 Male Population Age 57 (Esri) MAGE58_CY 2018 Male Population Age 58 (Esri) MAGE59_CY 2018 Male Population Age 59 (Esri) MAGE60_CY 2018 Male Population Age 60 (Esri) MAGE61_CY 2018 Male Population Age 61 (Esri) MAGE62_CY 2018 Male Population Age 62 (Esri) MAGE63_CY 2018 Male Population Age 63 (Esri) MAGE64_CY 2018 Male Population Age 64 (Esri) MAGE65_CY 2018 Male Population Age 65 (Esri) MAGE66_CY 2018 Male Population Age 66 (Esri) MAGE67_CY 2018 Male Population Age 67 (Esri) MAGE68_CY 2018 Male Population Age 68 (Esri) MAGE69_CY 2018 Male Population Age 69 (Esri) MAGE70_CY 2018 Male Population Age 70 (Esri) MAGE71_CY 2018 Male Population Age 71 (Esri) MAGE72_CY 2018 Male Population Age 72 (Esri) MAGE73_CY 2018 Male Population Age 73 (Esri) MAGE74_CY 2018 Male Population Age 74 (Esri) MAGE75_CY 2018 Male Population Age 75 (Esri) MAGE76_CY 2018 Male Population Age 76 (Esri) MAGE77_CY 2018 Male Population Age 77 (Esri) MAGE78_CY 2018 Male Population Age 78 (Esri) MAGE79_CY 2018 Male Population Age 79 (Esri) MAGE80_CY 2018 Male Population Age 80 (Esri) MAGE81_CY 2018 Male Population Age 81 (Esri) MAGE82_CY 2018 Male Population Age 82 (Esri) MAGE83_CY 2018 Male Population Age 83 (Esri) MAGE84_CY 2018 Male Population Age 84 (Esri) FAGE0_CY 2018 Female Population Age <1 (Esri) FAGE1_CY 2018 Female Population Age 1 (Esri) FAGE2_CY 2018 Female Population Age 2 (Esri) FAGE3_CY 2018 Female Population Age 3 (Esri) FAGE4_CY 2018 Female Population Age 4 (Esri) FAGE5_CY 2018 Female Population Age 5 (Esri) FAGE6_CY 2018 Female Population Age 6 (Esri) FAGE7_CY 2018 Female Population Age 7 (Esri) FAGE8_CY 2018 Female Population Age 8 (Esri) FAGE9_CY 2018 Female Population Age 9 (Esri) FAGE10_CY 2018 Female Population Age 10 (Esri) FAGE11_CY 2018 Female Population Age 11 (Esri) FAGE12_CY 2018 Female Population Age 12 (Esri) FAGE13_CY 2018 Female Population Age 13 (Esri) FAGE14_CY 2018 Female Population Age 14 (Esri) FAGE15_CY 2018 Female Population Age 15 (Esri) FAGE16_CY 2018 Female Population Age 16 (Esri) FAGE17_CY 2018 Female Population Age 17 (Esri) FAGE18_CY 2018 Female Population Age 18 (Esri) FAGE19_CY 2018 Female Population Age 19 (Esri) FAGE20_CY 2018 Female Population Age 20 (Esri) FAGE21_CY 2018 Female Population Age 21 (Esri) FAGE22_CY 2018 Female Population Age 22 (Esri) FAGE23_CY 2018 Female Population Age 23 (Esri) FAGE24_CY 2018 Female Population Age 24 (Esri) FAGE25_CY 2018 Female Population Age 25 (Esri) FAGE26_CY 2018 Female Population Age 26 (Esri) FAGE27_CY 2018 Female Population Age 27 (Esri) FAGE28_CY 2018 Female Population Age 28 (Esri) FAGE29_CY 2018 Female Population Age 29 (Esri) FAGE30_CY 2018 Female Population Age 30 (Esri) FAGE31_CY 2018 Female Population Age 31 (Esri) FAGE32_CY 2018 Female Population Age 32 (Esri) FAGE33_CY 2018 Female Population Age 33 (Esri) FAGE34_CY 2018 Female Population Age 34 (Esri) FAGE35_CY 2018 Female Population Age 35 (Esri) FAGE36_CY 2018 Female Population Age 36 (Esri) FAGE37_CY 2018 Female Population Age 37 (Esri) FAGE38_CY 2018 Female Population Age 38 (Esri) FAGE39_CY 2018 Female Population Age 39 (Esri) FAGE40_CY 2018 Female Population Age 40 (Esri) FAGE41_CY 2018 Female Population Age 41 (Esri) FAGE42_CY 2018 Female Population Age 42 (Esri) FAGE43_CY 2018 Female Population Age 43 (Esri) FAGE44_CY 2018 Female Population Age 44 (Esri) FAGE45_CY 2018 Female Population Age 45 (Esri) FAGE46_CY 2018 Female Population Age 46 (Esri) FAGE47_CY 2018 Female Population Age 47 (Esri) FAGE48_CY 2018 Female Population Age 48 (Esri) FAGE49_CY 2018 Female Population Age 49 (Esri) FAGE50_CY 2018 Female Population Age 50 (Esri) FAGE51_CY 2018 Female Population Age 51 (Esri) FAGE52_CY 2018 Female Population Age 52 (Esri) FAGE53_CY 2018 Female Population Age 53 (Esri) FAGE54_CY 2018 Female Population Age 54 (Esri) FAGE55_CY 2018 Female Population Age 55 (Esri) FAGE56_CY 2018 Female Population Age 56 (Esri) FAGE57_CY 2018 Female Population Age 57 (Esri) FAGE58_CY 2018 Female Population Age 58 (Esri) FAGE59_CY 2018 Female Population Age 59 (Esri) FAGE60_CY 2018 Female Population Age 60 (Esri) FAGE61_CY 2018 Female Population Age 61 (Esri) FAGE62_CY 2018 Female Population Age 62 (Esri) FAGE63_CY 2018 Female Population Age 63 (Esri) FAGE64_CY 2018 Female Population Age 64 (Esri) FAGE65_CY 2018 Female Population Age 65 (Esri) FAGE66_CY 2018 Female Population Age 66 (Esri) FAGE67_CY 2018 Female Population Age 67 (Esri) FAGE68_CY 2018 Female Population Age 68 (Esri) FAGE69_CY 2018 Female Population Age 69 (Esri) FAGE70_CY 2018 Female Population Age 70 (Esri) FAGE71_CY 2018 Female Population Age 71 (Esri) FAGE72_CY 2018 Female Population Age 72 (Esri) FAGE73_CY 2018 Female Population Age 73 (Esri) FAGE74_CY 2018 Female Population Age 74 (Esri) FAGE75_CY 2018 Female Population Age 75 (Esri) FAGE76_CY 2018 Female Population Age 76 (Esri) FAGE77_CY 2018 Female Population Age 77 (Esri) FAGE78_CY 2018 Female Population Age 78 (Esri) FAGE79_CY 2018 Female Population Age 79 (Esri) FAGE80_CY 2018 Female Population Age 80 (Esri) FAGE81_CY 2018 Female Population Age 81 (Esri) FAGE82_CY 2018 Female Population Age 82 (Esri) FAGE83_CY 2018 Female Population Age 83 (Esri) FAGE84_CY 2018 Female Population Age 84 (Esri) WAGEBASECY 2018 White Population by Age Base (Esri) WHTMBASECY 2018 White Male Population by Age Base (Esri) WHTFBASECY 2018 White Female Population by Age Base (Esri) BAGEBASECY 2018 Black Population by Age Base (Esri) BLKMBASECY 2018 Black Male Population by Age Base (Esri) BLKFBASECY 2018 Black Female Population by Age Base (Esri) IAGEBASECY 2018 American Indian/Alaska Native Population by Age Base (Esri) AIMBASE_CY 2018 American Indian/Alaska Native Male Population by Age Base (Esri) AIFBASE_CY 2018 American Indian/Alaska Native Female Population by Age Base (Esri) AAGEBASECY 2018 Asian Population by Age Base (Esri) ASNMBASECY 2018 Asian Male Population by Age Base (Esri) ASNFBASECY 2018 Asian Female Population by Age Base (Esri) PAGEBASECY 2018 Pacific Islander Population by Age Base (Esri) PIMBASE_CY 2018 Pacific Islander Male Population by Age Base (Esri) PIFBASE_CY 2018 Pacific Islander Female Population by Age Base (Esri) OAGEBASECY 2018 Other Race Population by Age Base (Esri) OTHMBASECY 2018 Other Race Male Population by Age Base (Esri) OTHFBASECY 2018 Other Race Female Population by Age Base (Esri) MAGEBASECY 2018 Multiple Races Population by Age Base (Esri) MLTMBASECY 2018 Multiple Races Male Population by Age Base (Esri) MLTFBASECY 2018 Multiple Races Female Population by Age Base (Esri) HAGEBASECY 2018 Hispanic Population by Age Base (Esri) HSPMBASECY 2018 Hispanic Male Population by Age Base (Esri) HSPFBASECY 2018 Hispanic Female Population by Age Base (Esri) CIVLBFR_CY 2018 Civilian Population Age 16+ in Labor Force (Esri) EMP_CY 2018 Employed Civilian Population Age 16+ (Esri) INDAGRI_CY 2018 Industry: Agriculture/Forestry/Fishing/Hunting (Esri) INDMIN_CY 2018 Industry: Mining/Quarrying/Oil & Gas Extraction (Esri) INDCONS_CY 2018 Industry: Construction (Esri) INDMANU_CY 2018 Industry: Manufacturing (Esri) INDWHTR_CY 2018 Industry: Wholesale Trade (Esri) INDRTTR_CY 2018 Industry: Retail Trade (Esri) INDTRAN_CY 2018 Industry: Transportation/Warehousing (Esri) INDUTIL_CY 2018 Industry: Utilities (Esri) INDINFO_CY 2018 Industry: Information (Esri) INDFIN_CY 2018 Industry: Finance/Insurance (Esri) INDRE_CY 2018 Industry: Real Estate/Rental/Leasing (Esri) INDTECH_CY 2018 Industry: Professional/Scientific/Tech Services (Esri) INDMGMT_CY 2018 Industry: Management of Companies/Enterprises (Esri) INDADMN_CY 2018 Industry: Admin/Support/Waste Management Services (Esri) INDEDUC_CY 2018 Industry: Educational Services (Esri) INDHLTH_CY 2018 Industry: Health Care/Social Assistance (Esri) INDARTS_CY 2018 Industry: Arts/Entertainment/Recreation (Esri) INDFOOD_CY 2018 Industry: Accommodation/Food Services (Esri) INDOTSV_CY 2018 Industry: Other Services (excl Public Administration) (Esri) INDPUBL_CY 2018 Industry: Public Administration (Esri) INDBASE_CY 2018 Employed Civilian Population Age 16+ by Industry Base (Esri) UNEMP_CY 2018 Unemployed Population Age 16+ (Esri) UNEMPRT_CY 2018 Unemployment Rate (Esri) OCCMGMT_CY 2018 Occupation: Management (Esri) OCCBUS_CY 2018 Occupation: Business/Financial (Esri) OCCCOMP_CY 2018 Occupation: Computer/Mathematical (Esri) OCCARCH_CY 2018 Occupation: Architecture/Engineering (Esri) OCCSSCI_CY 2018 Occupation: Life/Physical/Social Science (Esri) OCCSSRV_CY 2018 Occupation: Community/Social Service (Esri) OCCLEGL_CY 2018 Occupation: Legal (Esri) OCCEDUC_CY 2018 Occupation: Education/Training/Library (Esri) OCCENT_CY 2018 Occupation: Arts/Design/Entertainment/Sports/Media (Esri) OCCHTCH_CY 2018 Occupation: Healthcare Practitioner/Technician (Esri) OCCHLTH_CY 2018 Occupation: Healthcare Support (Esri) OCCPROT_CY 2018 Occupation: Protective Service (Esri) OCCFOOD_CY 2018 Occupation: Food Preparation/Serving Related (Esri) OCCBLDG_CY 2018 Occupation: Building/Grounds Cleaning/Maintenance (Esri) OCCPERS_CY 2018 Occupation: Personal Care/Service (Esri) OCCSALE_CY 2018 Occupation: Sales and Sales Related (Esri) OCCADMN_CY 2018 Occupation: Office/Administrative Support (Esri) OCCFARM_CY 2018 Occupation: Farming/Fishing/Forestry (Esri) OCCCONS_CY 2018 Occupation: Construction/Extraction (Esri) OCCMAIN_CY 2018 Occupation: Installation/Maintenance/Repair (Esri) OCCPROD_CY 2018 Occupation: Production (Esri) OCCTRAN_CY 2018 Occupation: Transportation/Material Moving (Esri) OCCBASE_CY 2018 Employed Civilian Population Age 16+ by Occupation Base (Esri) WHITE_CY 2018 White Population (Esri) BLACK_CY 2018 Black/African American Population (Esri) AMERIND_CY 2018 American Indian/Alaska Native Population (Esri) ASIAN_CY 2018 Asian Population (Esri) PACIFIC_CY 2018 Pacific Islander Population (Esri) OTHRACE_CY 2018 Other Race Population (Esri) RACE2UP_CY 2018 Population of Two or More Races (Esri) HISPPOP_CY 2018 Hispanic Population (Esri) HISPWHT_CY 2018 Hispanic White Population (Esri) HISPBLK_CY 2018 Hispanic Black/African American Population (Esri) HISPAI_CY 2018 Hispanic American Indian/Alaska Native Population (Esri) HISPASN_CY 2018 Hispanic Asian Population (Esri) HISPPI_CY 2018 Hispanic Pacific Islander Population (Esri) HISPOTH_CY 2018 Hispanic Other Race Population (Esri) HISPMLT_CY 2018 Hispanic Population of Two or More Races (Esri) NONHISP_CY 2018 Non-Hispanic Population (Esri) NHSPWHT_CY 2018 White Non-Hispanic Population (Esri) NHSPBLK_CY 2018 Black/African American Non-Hispanic Population (Esri) NHSPAI_CY 2018 American Indian/Alaska Native Non-Hispanic Population (Esri) NHSPASN_CY 2018 Asian Non-Hispanic Population (Esri) NHSPPI_CY 2018 Pacific Islander Non-Hispanic Population (Esri) NHSPOTH_CY 2018 Other Race Non-Hispanic Population (Esri) NHSPMLT_CY 2018 Multiple Races Non-Hispanic Population (Esri) MINORITYCY 2018 Minority Population (Esri) DIVINDX_CY 2018 Diversity Index (Esri) RACEBASECY 2018 Population by Race Base (Esri) NOHS_CY 2018 Education: Less than 9th Grade (Esri) SOMEHS_CY 2018 Education: 9-12th Grade/No Diploma (Esri) HSGRAD_CY 2018 Education: High School Diploma (Esri) GED_CY 2018 Education: GED/Alternative Credential (Esri) SMCOLL_CY 2018 Education: Some College/No Degree (Esri) ASSCDEG_CY 2018 Education: Associate's Degree (Esri) BACHDEG_CY 2018 Education: Bachelor's Degree (Esri) GRADDEG_CY 2018 Education: Graduate/Professional Degree (Esri) EDUCBASECY 2018 Educational Attainment Base (Esri) NEVMARR_CY 2018 Population Age 15+: Never Married (Esri) MARRIED_CY 2018 Population Age 15+: Married (Esri) WIDOWED_CY 2018 Population Age 15+: Widowed (Esri) DIVORCD_CY 2018 Population Age 15+: Divorced (Esri) MARBASE_CY 2018 Marital Status Base (Esri) HINC0_CY 2018 Household Income less than $15,000 (Esri) HINC15_CY 2018 Household Income $15,000-$24,999 (Esri) HINC25_CY 2018 Household Income $25,000-$34,999 (Esri) HINC35_CY 2018 Household Income $35,000-$49,999 (Esri) HINC50_CY 2018 Household Income $50,000-$74,999 (Esri) HINC75_CY 2018 Household Income $75,000-$99,999 (Esri) HINC100_CY 2018 Household Income $100,000-$149,999 (Esri) HINC150_CY 2018 Household Income $150,000-$199,999 (Esri) HINC200_CY 2018 Household Income $200,000 or greater (Esri) MEDHINC_CY 2018 Median Household Income (Esri) AVGHINC_CY 2018 Average Household Income (Esri) PCI_CY 2018 Per Capita Income (Esri) AGGINC_CY 2018 Aggregate Income (Esri) AGGHINC_CY 2018 Aggregate Household Income (Esri) HINCBASECY 2018 Households by Income Base (Esri) A15I0_CY 2018 Household Income less than $15,000 and Householder Age 15-24 (Esri) A15I15_CY 2018 Household Income $15,000-$24,999 and Householder Age 15-24 (Esri) A15I25_CY 2018 Household Income $25,000-$34,999 and Householder Age 15-24 (Esri) A15I35_CY 2018 Household Income $35,000-$49,999 and Householder Age 15-24 (Esri) A15I50_CY 2018 Household Income $50,000-$74,999 and Householder Age 15-24 (Esri) A15I75_CY 2018 Household Income $75,000-$99,999 and Householder Age 15-24 (Esri) A15I100_CY 2018 Household Income $100,000-$149,999 and Householder Age 15-24 (Esri) A15I150_CY 2018 Household Income $150,000-$199,999 and Householder Age 15-24 (Esri) A15I200_CY 2018 Household Income $200,000+ and Householder Age 15-24 (Esri) MEDIA15_CY 2018 Median Household Income and Householder Age 15-24 (Esri) AVGIA15_CY 2018 Average Household Income and Householder Age 15-24 (Esri) IA15BASECY 2018 Households by Income Base and Householder Age 15-24 (Esri) AGGIA15_CY 2018 Aggregate Household Income and Householder Age 15-24 (Esri) A25I0_CY 2018 Household Income less than $15,000 and Householder Age 25-34 (Esri) A25I15_CY 2018 Household Income $15,000-$24,999 and Householder Age 25-34 (Esri) A25I25_CY 2018 Household Income $25,000-$34,999 and Householder Age 25-34 (Esri) A25I35_CY 2018 Household Income $35,000-$49,999 and Householder Age 25-34 (Esri) A25I50_CY 2018 Household Income $50,000-$74,999 and Householder Age 25-34 (Esri) A25I75_CY 2018 Household Income $75,000-$99,999 and Householder Age 25-34 (Esri) A25I100_CY 2018 Household Income $100,000-$149,999 and Householder Age 25-34 (Esri) A25I150_CY 2018 Household Income $150,000-$199,999 and Householder Age 25-34 (Esri) A25I200_CY 2018 Household Income $200,000+ and Householder Age 25-34 (Esri) MEDIA25_CY 2018 Median Household Income and Householder Age 25-34 (Esri) AVGIA25_CY 2018 Average Household Income and Householder Age 25-34 (Esri) IA25BASECY 2018 Households by Income Base and Householder Age 25-34 (Esri) AGGIA25_CY 2018 Aggregate Household Income and Householder Age 25-34 (Esri) A35I0_CY 2018 Household Income less than $15,000 and Householder Age 35-44 (Esri) A35I15_CY 2018 Household Income $15,000-$24,999 and Householder Age 35-44 (Esri) A35I25_CY 2018 Household Income $25,000-$34,999 and Householder Age 35-44 (Esri) A35I35_CY 2018 Household Income $35,000-$49,999 and Householder Age 35-44 (Esri) A35I50_CY 2018 Household Income $50,000-$74,999 and Householder Age 35-44 (Esri) A35I75_CY 2018 Household Income $75,000-$99,999 and Householder Age 35-44 (Esri) A35I100_CY 2018 Household Income $100,000-$149,999 and Householder Age 35-44 (Esri) A35I150_CY 2018 Household Income $150,000-$199,999 and Householder Age 35-44 (Esri) A35I200_CY 2018 Household Income $200,000+ and Householder Age 35-44 (Esri) MEDIA35_CY 2018 Median Household Income and Householder Age 35-44 (Esri) AVGIA35_CY 2018 Average Household Income and Householder Age 35-44 (Esri) IA35BASECY 2018 Households by Income Base and Householder Age 35-44 (Esri) AGGIA35_CY 2018 Aggregate Household Income and Householder Age 35-44 (Esri) A45I0_CY 2018 Household Income less than $15,000 and Householder Age 45-54 (Esri) A45I15_CY 2018 Household Income $15,000-$24,999 and Householder Age 45-54 (Esri) A45I25_CY 2018 Household Income $25,000-$34,999 and Householder Age 45-54 (Esri) A45I35_CY 2018 Household Income $35,000-$49,999 and Householder Age 45-54 (Esri) A45I50_CY 2018 Household Income $50,000-$74,999 and Householder Age 45-54 (Esri) A45I75_CY 2018 Household Income $75,000-$99,999 and Householder Age 45-54 (Esri) A45I100_CY 2018 Household Income $100,000-$149,999 and Householder Age 45-54 (Esri) A45I150_CY 2018 Household Income $150,000-$199,999 and Householder Age 45-54 (Esri) A45I200_CY 2018 Household Income $200,000+ and Householder Age 45-54 (Esri) MEDIA45_CY 2018 Median Household Income and Householder Age 45-54 (Esri) AVGIA45_CY 2018 Average Household Income and Householder Age 45-54 (Esri) IA45BASECY 2018 Households by Income Base and Householder Age 45-54 (Esri) AGGIA45_CY 2018 Aggregate Household Income and Householder Age 45-54 (Esri) A55I0_CY 2018 Household Income less than $15,000 and Householder Age 55-64 (Esri) A55I15_CY 2018 Household Income $15,000-$24,999 and Householder Age 55-64 (Esri) A55I25_CY 2018 Household Income $25,000-$34,999 and Householder Age 55-64 (Esri) A55I35_CY 2018 Household Income $35,000-$49,999 and Householder Age 55-64 (Esri) A55I50_CY 2018 Household Income $50,000-$74,999 and Householder Age 55-64 (Esri) A55I75_CY 2018 Household Income $75,000-$99,999 and Householder Age 55-64 (Esri) A55I100_CY 2018 Household Income $100,000-$149,999 and Householder Age 55-64 (Esri) A55I150_CY 2018 Household Income $150,000-$199,999 and Householder Age 55-64 (Esri) A55I200_CY 2018 Household Income $200,000+ and Householder Age 55-64 (Esri) MEDIA55_CY 2018 Median Household Income and Householder Age 55-64 (Esri) AVGIA55_CY 2018 Average Household Income and Householder Age 55-64 (Esri) IA55BASECY 2018 Households by Income Base and Householder Age 55-64 (Esri) AGGIA55_CY 2018 Aggregate Household Income and Householder Age 55-64 (Esri) A65I0_CY 2018 Household Income less than $15,000 and Householder Age 65-74 (Esri) A65I15_CY 2018 Household Income $15,000-$24,999 and Householder Age 65-74 (Esri) A65I25_CY 2018 Household Income $25,000-$34,999 and Householder Age 65-74 (Esri) A65I35_CY 2018 Household Income $35,000-$49,999 and Householder Age 65-74 (Esri) A65I50_CY 2018 Household Income $50,000-$74,999 and Householder Age 65-74 (Esri) A65I75_CY 2018 Household Income $75,000-$99,999 and Householder Age 65-74 (Esri) A65I100_CY 2018 Household Income $100,000-$149,999 and Householder Age 65-74 (Esri) A65I150_CY 2018 Household Income $150,000-$199,999 and Householder Age 65-74 (Esri) A65I200_CY 2018 Household Income $200,000+ and Householder Age 65-74 (Esri) MEDIA65_CY 2018 Median Household Income and Householder Age 65-74 (Esri) AVGIA65_CY 2018 Average Household Income and Householder Age 65-74 (Esri) IA65BASECY 2018 Households by Income Base and Householder Age 65-74 (Esri) AGGIA65_CY 2018 Aggregate Household Income and Householder Age 65-74 (Esri) A75I0_CY 2018 Household Income less than $15,000 and Householder Age 75+ (Esri) A75I15_CY 2018 Household Income $15,000-$24,999 and Householder Age 75+ (Esri) A75I25_CY 2018 Household Income $25,000-$34,999 and Householder Age 75+ (Esri) A75I35_CY 2018 Household Income $35,000-$49,999 and Householder Age 75+ (Esri) A75I50_CY 2018 Household Income $50,000-$74,999 and Householder Age 75+ (Esri) A75I75_CY 2018 Household Income $75,000-$99,999 and Householder Age 75+ (Esri) A75I100_CY 2018 Household Income $100,000-$149,999 and Householder Age 75+ (Esri) A75I150_CY 2018 Household Income $150,000-$199,999 and Householder Age 75+ (Esri) A75I200_CY 2018 Household Income $200,000+ and Householder Age 75+ (Esri) MEDIA75_CY 2018 Median Household Income and Householder Age 75+ (Esri) AVGIA75_CY 2018 Average Household Income and Householder Age 75+ (Esri) IA75BASECY 2018 Households by Income Base and Householder Age 75+ (Esri) AGGIA75_CY 2018 Aggregate Household Income and Householder Age 75+ (Esri) MEDHHR_CY 2018 Median Age of Householder (Esri) MEDIA55UCY 2018 Median Household Income and Householder Age 55+ (Esri) AVGIA55UCY 2018 Average Household Income and Householder Age 55+ (Esri) IA55UBASCY 2018 Households by Income Base and Householder Age 55+ (Esri) MEDIA65UCY 2018 Median Household Income and Householder Age 65+ (Esri) AVGIA65UCY 2018 Average Household Income and Householder Age 65+ (Esri) IA65UBASCY 2018 Households by Income Base and Householder Age 65+ (Esri) DI0_CY 2018 Disposable Income less than $15,000 (Esri) DI15_CY 2018 Disposable Income $15,000-$24,999 (Esri) DI25_CY 2018 Disposable Income $25,000-$34,999 (Esri) DI35_CY 2018 Disposable Income $35,000-$49,999 (Esri) DI50_CY 2018 Disposable Income $50,000-$74,999 (Esri) DI75_CY 2018 Disposable Income $75,000-$99,999 (Esri) DI100_CY 2018 Disposable Income $100,000-$149,999 (Esri) DI150_CY 2018 Disposable Income $150,000-$199,999 (Esri) DI200_CY 2018 Disposable Income $200,000 or greater (Esri) AGGDI_CY 2018 Aggregate Disposable Income MEDDI_CY 2018 Median Disposable Income (Esri) AVGDI_CY 2018 Average Disposable Income (Esri) A15DI0_CY 2018 Disposable Income less than $15,000 and Householder Age 15-24 (Esri) A15DI15_CY 2018 Disposable Income $15,000-$24,999 and Householder Age 15-24 (Esri) A15DI25_CY 2018 Disposable Income $25,000-$34,999 and Householder Age 15-24 (Esri) A15DI35_CY 2018 Disposable Income $35,000-$49,999 and Householder Age 15-24 (Esri) A15DI50_CY 2018 Disposable Income $50,000-$74,999 and Householder Age 15-24 (Esri) A15DI75_CY 2018 Disposable Income $75,000-$99,999 and Householder Age 15-24 (Esri) A15DI100CY 2018 Disposable Income $100,000-$149,999 and Householder Age 15-24 (Esri) A15DI150CY 2018 Disposable Income $150,000-$199,999 and Householder Age 15-24 (Esri) A15DI200CY 2018 Disposable Income $200,000+ and Householder Age 15-24 (Esri) AGGDIA15CY 2018 Aggregate Disposable Income: Householder 15-24 MEDDIA15CY 2018 Median Disposable Income and Householder Age 15-24 (Esri) AVGDIA15CY 2018 Average Disposable Income and Householder Age 15-24 (Esri) A25DI0_CY 2018 Disposable Income less than $15,000 and Householder Age 25-34 (Esri) A25DI15_CY 2018 Disposable Income $15,000-$24,999 and Householder Age 25-34 (Esri) A25DI25_CY 2018 Disposable Income $25,000-$34,999 and Householder Age 25-34 (Esri) A25DI35_CY 2018 Disposable Income $35,000-$49,999 and Householder Age 25-34 (Esri) A25DI50_CY 2018 Disposable Income $50,000-$74,999 and Householder Age 25-34 (Esri) A25DI75_CY 2018 Disposable Income $75,000-$99,999 and Householder Age 25-34 (Esri) A25DI100CY 2018 Disposable Income $100,000-$149,999 and Householder Age 25-34 (Esri) A25DI150CY 2018 Disposable Income $150,000-$199,999 and Householder Age 25-34 (Esri) A25DI200CY 2018 Disposable Income $200,000+ and Householder Age 25-34 (Esri) AGGDIA25CY 2018 Aggregate Disposable Income: Householder 25-34 MEDDIA25CY 2018 Median Disposable Income and Householder Age 25-34 (Esri) AVGDIA25CY 2018 Average Disposable Income and Householder Age 25-34 (Esri) A35DI0_CY 2018 Disposable Income less than $15,000 and Householder Age 35-44 (Esri) A35DI15_CY 2018 Disposable Income $15,000-$24,999 and Householder Age 35-44 (Esri) A35DI25_CY 2018 Disposable Income $25,000-$34,999 and Householder Age 35-44 (Esri) A35DI35_CY 2018 Disposable Income $35,000-$49,999 and Householder Age 35-44 (Esri) A35DI50_CY 2018 Disposable Income $50,000-$74,999 and Householder Age 35-44 (Esri) A35DI75_CY 2018 Disposable Income $75,000-$99,999 and Householder Age 35-44 (Esri) A35DI100CY 2018 Disposable Income $100,000-$149,999 and Householder Age 35-44 (Esri) A35DI150CY 2018 Disposable Income $150,000-$199,999 and Householder Age 35-44 (Esri) A35DI200CY 2018 Disposable Income $200,000+ and Householder Age 35-44 (Esri) AGGDIA35CY 2018 Aggregate Disposable Income: Householder 35-44 MEDDIA35CY 2018 Median Disposable Income and Householder Age 35-44 (Esri) AVGDIA35CY 2018 Average Disposable Income and Householder Age 35-44 (Esri) A45DI0_CY 2018 Disposable Income less than $15,000 and Householder Age 45-54 (Esri) A45DI15_CY 2018 Disposable Income $15,000-$24,999 and Householder Age 45-54 (Esri) A45DI25_CY 2018 Disposable Income $25,000-$34,999 and Householder Age 45-54 (Esri) A45DI35_CY 2018 Disposable Income $35,000-$49,999 and Householder Age 45-54 (Esri) A45DI50_CY 2018 Disposable Income $50,000-$74,999 and Householder Age 45-54 (Esri) A45DI75_CY 2018 Disposable Income $75,000-$99,999 and Householder Age 45-54 (Esri) A45DI100CY 2018 Disposable Income $100,000-$149,999 and Householder Age 45-54 (Esri) A45DI150CY 2018 Disposable Income $150,000-$199,999 and Householder Age 45-54 (Esri) A45DI200CY 2018 Disposable Income $200,000+ and Householder Age 45-54 (Esri) AGGDIA45CY 2018 Aggregate Disposable Income: Householder 45-54 MEDDIA45CY 2018 Median Disposable Income and Householder Age 45-54 (Esri) AVGDIA45CY 2018 Average Disposable Income and Householder Age 45-54 (Esri) A55DI0_CY 2018 Disposable Income less than $15,000 and Householder Age 55-64 (Esri) A55DI15_CY 2018 Disposable Income $15,000-$24,999 and Householder Age 55-64 (Esri) A55DI25_CY 2018 Disposable Income $25,000-$34,999 and Householder Age 55-64 (Esri) A55DI35_CY 2018 Disposable Income $35,000-$49,999 and Householder Age 55-64 (Esri) A55DI50_CY 2018 Disposable Income $50,000-$74,999 and Householder Age 55-64 (Esri) A55DI75_CY 2018 Disposable Income $75,000-$99,999 and Householder Age 55-64 (Esri) A55DI100CY 2018 Disposable Income $100,000-$149,999 and Householder Age 55-64 (Esri) A55DI150CY 2018 Disposable Income $150,000-$199,999 and Householder Age 55-64 (Esri) A55DI200CY 2018 Disposable Income $200,000+ and Householder Age 55-64 (Esri) AGGDIA55CY 2018 Aggregate Disposable Income: Householder 55-64 MEDDIA55CY 2018 Median Disposable Income and Householder Age 55-64 (Esri) AVGDIA55CY 2018 Average Disposable Income and Householder Age 55-64 (Esri) A65DI0_CY 2018 Disposable Income less than $15,000 and Householder Age 65-74 (Esri) A65DI15_CY 2018 Disposable Income $15,000-$24,999 and Householder Age 65-74 (Esri) A65DI25_CY 2018 Disposable Income $25,000-$34,999 and Householder Age 65-74 (Esri) A65DI35_CY 2018 Disposable Income $35,000-$49,999 and Householder Age 65-74 (Esri) A65DI50_CY 2018 Disposable Income $50,000-$74,999 and Householder Age 65-74 (Esri) A65DI75_CY 2018 Disposable Income $75,000-$99,999 and Householder Age 65-74 (Esri) A65DI100CY 2018 Disposable Income $100,000-$149,999 and Householder Age 65-74 (Esri) A65DI150CY 2018 Disposable Income $150,000-$199,999 and Householder Age 65-74 (Esri) A65DI200CY 2018 Disposable Income $200,000+ and Householder Age 65-74 (Esri) AGGDIA65CY 2018 Aggregate Disposable Income: Householder 65-74 MEDDIA65CY 2018 Median Disposable Income and Householder Age 65-74 (Esri) AVGDIA65CY 2018 Average Disposable Income and Householder Age 65-74 (Esri) A75DI0_CY 2018 Disposable Income less than $15,000 and Householder Age 75+ (Esri) A75DI15_CY 2018 Disposable Income $15,000-$24,999 and Householder Age 75+ (Esri) A75DI25_CY 2018 Disposable Income $25,000-$34,999 and Householder Age 75+ (Esri) A75DI35_CY 2018 Disposable Income $35,000-$49,999 and Householder Age 75+ (Esri) A75DI50_CY 2018 Disposable Income $50,000-$74,999 and Householder Age 75+ (Esri) A75DI75_CY 2018 Disposable Income $75,000-$99,999 and Householder Age 75+ (Esri) A75DI100CY 2018 Disposable Income $100,000-$149,999 and Householder Age 75+ (Esri) A75DI150CY 2018 Disposable Income $150,000-$199,999 and Householder Age 75+ (Esri) A75DI200CY 2018 Disposable Income $200,000 or greater and Householder Age 75+ (Esri) AGGDIA75CY 2018 Aggregate Disposable Income: Householder 75+ MEDDIA75CY 2018 Median Disposable Income and Householder Age 75+ (Esri) AVGDIA75CY 2018 Average Disposable Income and Householder Age 75+ (Esri) DIA15BASCY 2018 HHs by Disposable Income Base and Householder Age 15-24 (Esri) DIA25BASCY 2018 HHs by Disposable Income Base and Householder Age 25-34 (Esri) DIA35BASCY 2018 HHs by Disposable Income Base and Householder Age 35-44 (Esri) DIA45BASCY 2018 HHs by Disposable Income Base and Householder Age 45-54 (Esri) DIA55BASCY 2018 HHs by Disposable Income Base and Householder Age 55-64 (Esri) DIA65BASCY 2018 HHs by Disposable Income Base and Householder Age 65-74 (Esri) DIA75BASCY 2018 Households by Disposable Income Base and Householder Age 75+ (Esri) DIBASE_CY 2018 Households by Disposable Income Base (Esri) NW0_CY 2018 Net Worth less than $15,000 (Esri) NW15_CY 2018 Net Worth $15,000-$34,999 (Esri) NW35_CY 2018 Net Worth $35,000-$49,999 (Esri) NW50_CY 2018 Net Worth $50,000-$74,999 (Esri) NW75_CY 2018 Net Worth $75,000-$99,999 (Esri) NW100_CY 2018 Net Worth $100,000-$149,999 (Esri) NW150_CY 2018 Net Worth $150,000-$249,999 (Esri) NW250_CY 2018 Net Worth $250,000-$499,999 (Esri) NW500_CY 2018 Net Worth $500,000 or greater (Esri) AGGNW_CY 2018 Aggregate Net Worth MEDNW_CY 2018 Median Net Worth (Esri) AVGNW_CY 2018 Average Net Worth (Esri) NWBASE_CY 2018 Households by Net Worth Base (Esri) A15NW0_CY 2018 Net Worth less than $15,000 and Householder Age 15-24 (Esri) A15NW15_CY 2018 Net Worth $15,000-$34,999 and Householder Age 15-24 (Esri) A15NW35_CY 2018 Net Worth $35,000-$49,999 and Householder Age 15-24 (Esri) A15NW50_CY 2018 Net Worth $50,000-$99,999 and Householder Age 15-24 (Esri) A15NW100CY 2018 Net Worth $100,000-$149,999 and Householder Age 15-24 (Esri) A15NW150CY 2018 Net Worth $150,000-$249,999 and Householder Age 15-24 (Esri) A15NW250CY 2018 Net Worth $250,000 or greater and Householder Age 15-24 (Esri) AGGNWA15CY 2018 Aggregate Net Worth: Householder 15-24 MEDNWA15CY 2018 Median Net Worth and Householder Age 15-24 (Esri) AVGNWA15CY 2018 Average Net Worth and Householder Age 15-24 (Esri) A25NW0_CY 2018 Net Worth less than $15,000 and Householder Age 25-34 (Esri) A25NW15_CY 2018 Net Worth $15,000-$34,999 and Householder Age 25-34 (Esri) A25NW35_CY 2018 Net Worth $35,000-$49,999 and Householder Age 25-34 (Esri) A25NW50_CY 2018 Net Worth $50,000-$99,999 and Householder Age 25-34 (Esri) A25NW100CY 2018 Net Worth $100,000-$149,999 and Householder Age 25-34 (Esri) A25NW150CY 2018 Net Worth $150,000-$249,999 and Householder Age 25-34 (Esri) A25NW250CY 2018 Net Worth $250,000 or greater and Householder Age 25-34 (Esri) AGGNWA25CY 2018 Aggregate Net Worth: Householder 25-34 MEDNWA25CY 2018 Median Net Worth and Householder Age 25-34 (Esri) AVGNWA25CY 2018 Average Net Worth and Householder Age 25-34 (Esri) A35NW0_CY 2018 Net Worth less than $15,000 and Householder Age 35-44 (Esri) A35NW15_CY 2018 Net Worth $15,000-$34,999 and Householder Age 35-44 (Esri) A35NW35_CY 2018 Net Worth $35,000-$49,999 and Householder Age 35-44 (Esri) A35NW50_CY 2018 Net Worth $50,000-$99,999 and Householder Age 35-44 (Esri) A35NW100CY 2018 Net Worth $100,000-$149,999 and Householder Age 35-44 (Esri) A35NW150CY 2018 Net Worth $150,000-$249,999 and Householder Age 35-44 (Esri) A35NW250CY 2018 Net Worth $250,000 or greater and Householder Age 35-44 (Esri) AGGNWA35CY 2018 Aggregate Net Worth: Householder 35-44 MEDNWA35CY 2018 Median Net Worth and Householder Age 35-44 (Esri) AVGNWA35CY 2018 Average Net Worth and Householder Age 35-44 (Esri) A45NW0_CY 2018 Net Worth less than $15,000 and Householder Age 45-54 (Esri) A45NW15_CY 2018 Net Worth $15,000-$34,999 and Householder Age 45-54 (Esri) A45NW35_CY 2018 Net Worth $35,000-$49,999 and Householder Age 45-54 (Esri) A45NW50_CY 2018 Net Worth $50,000-$99,999 and Householder Age 45-54 (Esri) A45NW100CY 2018 Net Worth $100,000-$149,999 and Householder Age 45-54 (Esri) A45NW150CY 2018 Net Worth $150,000-$249,999 and Householder Age 45-54 (Esri) A45NW250CY 2018 Net Worth $250,000 or greater and Householder Age 45-54 (Esri) AGGNWA45CY 2018 Aggregate Net Worth: Householder 45-54 MEDNWA45CY 2018 Median Net Worth and Householder Age 45-54 (Esri) AVGNWA45CY 2018 Average Net Worth and Householder Age 45-54 (Esri) A55NW0_CY 2018 Net Worth less than $15,000 and Householder Age 55-64 (Esri) A55NW15_CY 2018 Net Worth $15,000-$34,999 and Householder Age 55-64 (Esri) A55NW35_CY 2018 Net Worth $35,000-$49,999 and Householder Age 55-64 (Esri) A55NW50_CY 2018 Net Worth $50,000-$99,999 and Householder Age 55-64 (Esri) A55NW100CY 2018 Net Worth $100,000-$149,999 and Householder Age 55-64 (Esri) A55NW150CY 2018 Net Worth $150,000-$249,999 and Householder Age 55-64 (Esri) A55NW250CY 2018 Net Worth $250,000 or greater and Householder Age 55-64 (Esri) AGGNWA55CY 2018 Aggregate Net Worth: Householder 55-64 MEDNWA55CY 2018 Median Net Worth and Householder Age 55-64 (Esri) AVGNWA55CY 2018 Average Net Worth and Householder Age 55-64 (Esri) A65NW0_CY 2018 Net Worth less than $15,000 and Householder Age 65-74 (Esri) A65NW15_CY 2018 Net Worth $15,000-$34,999 and Householder Age 65-74 (Esri) A65NW35_CY 2018 Net Worth $35,000-$49,999 and Householder Age 65-74 (Esri) A65NW50_CY 2018 Net Worth $50,000-$99,999 and Householder Age 65-74 (Esri) A65NW100CY 2018 Net Worth $100,000-$149,999 and Householder Age 65-74 (Esri) A65NW150CY 2018 Net Worth $150,000-$249,999 and Householder Age 65-74 (Esri) A65NW250CY 2018 Net Worth $250,000 or greater and Householder Age 65-74 (Esri) AGGNWA65CY 2018 Aggregate Net Worth: Householder 65-74 MEDNWA65CY 2018 Median Net Worth and Householder Age 65-74 (Esri) AVGNWA65CY 2018 Average Net Worth and Householder Age 65-74 (Esri) A75NW0_CY 2018 Net Worth less than $15,000 and Householder Age 75+ (Esri) A75NW15_CY 2018 Net Worth $15,000-$34,999 and Householder Age 75+ (Esri) A75NW35_CY 2018 Net Worth $35,000-$49,999 and Householder Age 75+ (Esri) A75NW50_CY 2018 Net Worth $50,000-$99,999 and Householder Age 75+ (Esri) A75NW100CY 2018 Net Worth $100,000-$149,999 and Householder Age 75+ (Esri) A75NW150CY 2018 Net Worth $150,000-$249,999 and Householder Age 75+ (Esri) A75NW250CY 2018 Net Worth $250,000 or greater and Householder Age 75+ (Esri) AGGNWA75CY 2018 Aggregate Net Worth: Householder 75+ MEDNWA75CY 2018 Median Net Worth and Householder Age 75+ (Esri) AVGNWA75CY 2018 Average Net Worth and Householder Age 75+ (Esri) NWA15BASCY 2018 Households by Net Worth Base and Householder Age 15-24 (Esri) NWA25BASCY 2018 Households by Net Worth Base and Householder Age 25-34 (Esri) NWA35BASCY 2018 Households by Net Worth Base and Householder Age 35-44 (Esri) NWA45BASCY 2018 Households by Net Worth Base and Householder Age 45-54 (Esri) NWA55BASCY 2018 Households by Net Worth Base and Householder Age 55-64 (Esri) NWA65BASCY 2018 Households by Net Worth Base and Householder Age 65-74 (Esri) NWA75BASCY 2018 Households by Net Worth Base and Householder Age 75+ (Esri) VAL0_CY 2018 Home Value less than $50,000 (Esri) VAL50K_CY 2018 Home Value $50,000-$99,999 (Esri) VAL100K_CY 2018 Home Value $100,000-$149,999 (Esri) VAL150K_CY 2018 Home Value $150,000-$199,999 (Esri) VAL200K_CY 2018 Home Value $200,000-$249,999 (Esri) VAL250K_CY 2018 Home Value $250,000-$299,999 (Esri) VAL300K_CY 2018 Home Value $300,000-$399,999 (Esri) VAL400K_CY 2018 Home Value $400,000-$499,999 (Esri) VAL500K_CY 2018 Home Value $500,000-$749,999 (Esri) VAL750K_CY 2018 Home Value $750,000-$999,999 (Esri) VAL1M_CY 2018 Home Value $1,000,000-$1,499,999 (Esri) VAL1PT5MCY 2018 Home Value $1,500,000-$1,999,999 (Esri) VAL2M_CY 2018 Home Value $2,000,000 or greater (Esri) VALBASE_CY 2018 Owner Occupied Housing Units by Value Base (Esri) MEDVAL_CY 2018 Median Home Value (Esri) AVGVAL_CY 2018 Average Home Value (Esri) LANDAREA Land Area in Square Miles TOTPOP_FY 2023 Total Population (Esri) HHPOP_FY 2023 Household Population (Esri) FAMPOP_FY 2023 Family Population (Esri) GQPOP_FY 2023 Group Quarters Population (Esri) POPDENS_FY 2023 Population Density (Pop per Square Mile) (Esri) TOTHH_FY 2023 Total Households (Esri) AVGHHSZ_FY 2023 Average Household Size (Esri) FAMHH_FY 2023 Total Family Households (Esri) AVGFMSZ_FY 2023 Average Family Size (Esri) TOTHU_FY 2023 Total Housing Units (Esri) OWNER_FY 2023 Owner Occupied Housing Units (Esri) RENTER_FY 2023 Renter Occupied Housing Units (Esri) VACANT_FY 2023 Vacant Housing Units (Esri) POPGRWCYFY 2018-2023 Population: Annual Growth Rate (Esri) HHGRWCYFY 2018-2023 Households: Annual Growth Rate (Esri) FAMGRWCYFY 2018-2023 Families: Annual Growth Rate (Esri) PCIGRWCYFY 2018-2023 Per Capita Income: Annual Growth Rate (Esri) OWNGRWCYFY 2018-2023 Owner Occupied Housing Units Annual Compound Growth Rate (Esri) MHIGRWCYFY 2018-2023 Median Household Income: Annual Growth Rate (Esri) GENALPHAFY 2023 Generation Alpha Population (Born 2017 or Later) GENZ_FY 2023 Generation Z Population (Born 1999 to 2016) MILLENN_FY 2023 Millennial Population (Born 1981 to 1998) GENX_FY 2023 Generation X Population (Born 1965 to 1980) BABYBOOMFY 2023 Baby Boomer Population (Born 1946 to 1964) OLDRGENSFY 2023 Silent & Greatest Generations Population (Born 1945/Earlier) GENBASE_FY 2023 Population by Generation Base POP0_FY 2023 Total Population Age 0-4 (Esri) POP5_FY 2023 Total Population Age 5-9 (Esri) POP10_FY 2023 Total Population Age 10-14 (Esri) POP15_FY 2023 Total Population Age 15-19 (Esri) POP20_FY 2023 Total Population Age 20-24 (Esri) POP25_FY 2023 Total Population Age 25-29 (Esri) POP30_FY 2023 Total Population Age 30-34 (Esri) POP35_FY 2023 Total Population Age 35-39 (Esri) POP40_FY 2023 Total Population Age 40-44 (Esri) POP45_FY 2023 Total Population Age 45-49 (Esri) POP50_FY 2023 Total Population Age 50-54 (Esri) POP55_FY 2023 Total Population Age 55-59 (Esri) POP60_FY 2023 Total Population Age 60-64 (Esri) POP65_FY 2023 Total Population Age 65-69 (Esri) POP70_FY 2023 Total Population Age 70-74 (Esri) POP75_FY 2023 Total Population Age 75-79 (Esri) POP80_FY 2023 Total Population Age 80-84 (Esri) POP85_FY 2023 Total Population Age 85+ (Esri) POP18UP_FY 2023 Total Population Age 18+ (Esri) POP21UP_FY 2023 Total Population Age 21+ (Esri) MEDAGE_FY 2023 Median Age (Esri) MALES_FY 2023 Male Population (Esri) MALE0_FY 2023 Male Population Age 0-4 (Esri) MALE5_FY 2023 Male Population Age 5-9 (Esri) MALE10_FY 2023 Male Population Age 10-14 (Esri) MALE15_FY 2023 Male Population Age 15-19 (Esri) MALE20_FY 2023 Male Population Age 20-24 (Esri) MALE25_FY 2023 Male Population Age 25-29 (Esri) MALE30_FY 2023 Male Population Age 30-34 (Esri) MALE35_FY 2023 Male Population Age 35-39 (Esri) MALE40_FY 2023 Male Population Age 40-44 (Esri) MALE45_FY 2023 Male Population Age 45-49 (Esri) MALE50_FY 2023 Male Population Age 50-54 (Esri) MALE55_FY 2023 Male Population Age 55-59 (Esri) MALE60_FY 2023 Male Population Age 60-64 (Esri) MALE65_FY 2023 Male Population Age 65-69 (Esri) MALE70_FY 2023 Male Population Age 70-74 (Esri) MALE75_FY 2023 Male Population Age 75-79 (Esri) MALE80_FY 2023 Male Population Age 80-84 (Esri) MALE85_FY 2023 Male Population Age 85+ (Esri) MAL18UP_FY 2023 Male Population Age 18+ (Esri) MAL21UP_FY 2023 Male Population Age 21+ (Esri) MEDMAGE_FY 2023 Median Male Age (Esri) FEMALES_FY 2023 Female Population (Esri) FEM0_FY 2023 Female Population Age 0-4 (Esri) FEM5_FY 2023 Female Population Age 5-9 (Esri) FEM10_FY 2023 Female Population Age 10-14 (Esri) FEM15_FY 2023 Female Population Age 15-19 (Esri) FEM20_FY 2023 Female Population Age 20-24 (Esri) FEM25_FY 2023 Female Population Age 25-29 (Esri) FEM30_FY 2023 Female Population Age 30-34 (Esri) FEM35_FY 2023 Female Population Age 35-39 (Esri) FEM40_FY 2023 Female Population Age 40-44 (Esri) FEM45_FY 2023 Female Population Age 45-49 (Esri) FEM50_FY 2023 Female Population Age 50-54 (Esri) FEM55_FY 2023 Female Population Age 55-59 (Esri) FEM60_FY 2023 Female Population Age 60-64 (Esri) FEM65_FY 2023 Female Population Age 65-69 (Esri) FEM70_FY 2023 Female Population Age 70-74 (Esri) FEM75_FY 2023 Female Population Age 75-79 (Esri) FEM80_FY 2023 Female Population Age 80-84 (Esri) FEM85_FY 2023 Female Population Age 85+ (Esri) FEM18UP_FY 2023 Female Population Age 18+ (Esri) FEM21UP_FY 2023 Female Population Age 21+ (Esri) MEDFAGE_FY 2023 Median Female Age (Esri) AGEBASE_FY 2023 Total Population by Five-Year Age Base (Esri) AGE0_FY 2023 Total Population Age <1 (Esri) AGE1_FY 2023 Total Population Age 1 (Esri) AGE2_FY 2023 Total Population Age 2 (Esri) AGE3_FY 2023 Total Population Age 3 (Esri) AGE4_FY 2023 Total Population Age 4 (Esri) AGE5_FY 2023 Total Population Age 5 (Esri) AGE6_FY 2023 Total Population Age 6 (Esri) AGE7_FY 2023 Total Population Age 7 (Esri) AGE8_FY 2023 Total Population Age 8 (Esri) AGE9_FY 2023 Total Population Age 9 (Esri) AGE10_FY 2023 Total Population Age 10 (Esri) AGE11_FY 2023 Total Population Age 11 (Esri) AGE12_FY 2023 Total Population Age 12 (Esri) AGE13_FY 2023 Total Population Age 13 (Esri) AGE14_FY 2023 Total Population Age 14 (Esri) AGE15_FY 2023 Total Population Age 15 (Esri) AGE16_FY 2023 Total Population Age 16 (Esri) AGE17_FY 2023 Total Population Age 17 (Esri) AGE18_FY 2023 Total Population Age 18 (Esri) AGE19_FY 2023 Total Population Age 19 (Esri) AGE20_FY 2023 Total Population Age 20 (Esri) AGE21_FY 2023 Total Population Age 21 (Esri) AGE22_FY 2023 Total Population Age 22 (Esri) AGE23_FY 2023 Total Population Age 23 (Esri) AGE24_FY 2023 Total Population Age 24 (Esri) AGE25_FY 2023 Total Population Age 25 (Esri) AGE26_FY 2023 Total Population Age 26 (Esri) AGE27_FY 2023 Total Population Age 27 (Esri) AGE28_FY 2023 Total Population Age 28 (Esri) AGE29_FY 2023 Total Population Age 29 (Esri) AGE30_FY 2023 Total Population Age 30 (Esri) AGE31_FY 2023 Total Population Age 31 (Esri) AGE32_FY 2023 Total Population Age 32 (Esri) AGE33_FY 2023 Total Population Age 33 (Esri) AGE34_FY 2023 Total Population Age 34 (Esri) AGE35_FY 2023 Total Population Age 35 (Esri) AGE36_FY 2023 Total Population Age 36 (Esri) AGE37_FY 2023 Total Population Age 37 (Esri) AGE38_FY 2023 Total Population Age 38 (Esri) AGE39_FY 2023 Total Population Age 39 (Esri) AGE40_FY 2023 Total Population Age 40 (Esri) AGE41_FY 2023 Total Population Age 41 (Esri) AGE42_FY 2023 Total Population Age 42 (Esri) AGE43_FY 2023 Total Population Age 43 (Esri) AGE44_FY 2023 Total Population Age 44 (Esri) AGE45_FY 2023 Total Population Age 45 (Esri) AGE46_FY 2023 Total Population Age 46 (Esri) AGE47_FY 2023 Total Population Age 47 (Esri) AGE48_FY 2023 Total Population Age 48 (Esri) AGE49_FY 2023 Total Population Age 49 (Esri) AGE50_FY 2023 Total Population Age 50 (Esri) AGE51_FY 2023 Total Population Age 51 (Esri) AGE52_FY 2023 Total Population Age 52 (Esri) AGE53_FY 2023 Total Population Age 53 (Esri) AGE54_FY 2023 Total Population Age 54 (Esri) AGE55_FY 2023 Total Population Age 55 (Esri) AGE56_FY 2023 Total Population Age 56 (Esri) AGE57_FY 2023 Total Population Age 57 (Esri) AGE58_FY 2023 Total Population Age 58 (Esri) AGE59_FY 2023 Total Population Age 59 (Esri) AGE60_FY 2023 Total Population Age 60 (Esri) AGE61_FY 2023 Total Population Age 61 (Esri) AGE62_FY 2023 Total Population Age 62 (Esri) AGE63_FY 2023 Total Population Age 63 (Esri) AGE64_FY 2023 Total Population Age 64 (Esri) AGE65_FY 2023 Total Population Age 65 (Esri) AGE66_FY 2023 Total Population Age 66 (Esri) AGE67_FY 2023 Total Population Age 67 (Esri) AGE68_FY 2023 Total Population Age 68 (Esri) AGE69_FY 2023 Total Population Age 69 (Esri) AGE70_FY 2023 Total Population Age 70 (Esri) AGE71_FY 2023 Total Population Age 71 (Esri) AGE72_FY 2023 Total Population Age 72 (Esri) AGE73_FY 2023 Total Population Age 73 (Esri) AGE74_FY 2023 Total Population Age 74 (Esri) AGE75_FY 2023 Total Population Age 75 (Esri) AGE76_FY 2023 Total Population Age 76 (Esri) AGE77_FY 2023 Total Population Age 77 (Esri) AGE78_FY 2023 Total Population Age 78 (Esri) AGE79_FY 2023 Total Population Age 79 (Esri) AGE80_FY 2023 Total Population Age 80 (Esri) AGE81_FY 2023 Total Population Age 81 (Esri) AGE82_FY 2023 Total Population Age 82 (Esri) AGE83_FY 2023 Total Population Age 83 (Esri) AGE84_FY 2023 Total Population Age 84 (Esri) MAGE0_FY 2023 Male Population Age <1 (Esri) MAGE1_FY 2023 Male Population Age 1 (Esri) MAGE2_FY 2023 Male Population Age 2 (Esri) MAGE3_FY 2023 Male Population Age 3 (Esri) MAGE4_FY 2023 Male Population Age 4 (Esri) MAGE5_FY 2023 Male Population Age 5 (Esri) MAGE6_FY 2023 Male Population Age 6 (Esri) MAGE7_FY 2023 Male Population Age 7 (Esri) MAGE8_FY 2023 Male Population Age 8 (Esri) MAGE9_FY 2023 Male Population Age 9 (Esri) MAGE10_FY 2023 Male Population Age 10 (Esri) MAGE11_FY 2023 Male Population Age 11 (Esri) MAGE12_FY 2023 Male Population Age 12 (Esri) MAGE13_FY 2023 Male Population Age 13 (Esri) MAGE14_FY 2023 Male Population Age 14 (Esri) MAGE15_FY 2023 Male Population Age 15 (Esri) MAGE16_FY 2023 Male Population Age 16 (Esri) MAGE17_FY 2023 Male Population Age 17 (Esri) MAGE18_FY 2023 Male Population Age 18 (Esri) MAGE19_FY 2023 Male Population Age 19 (Esri) MAGE20_FY 2023 Male Population Age 20 (Esri) MAGE21_FY 2023 Male Population Age 21 (Esri) MAGE22_FY 2023 Male Population Age 22 (Esri) MAGE23_FY 2023 Male Population Age 23 (Esri) MAGE24_FY 2023 Male Population Age 24 (Esri) MAGE25_FY 2023 Male Population Age 25 (Esri) MAGE26_FY 2023 Male Population Age 26 (Esri) MAGE27_FY 2023 Male Population Age 27 (Esri) MAGE28_FY 2023 Male Population Age 28 (Esri) MAGE29_FY 2023 Male Population Age 29 (Esri) MAGE30_FY 2023 Male Population Age 30 (Esri) MAGE31_FY 2023 Male Population Age 31 (Esri) MAGE32_FY 2023 Male Population Age 32 (Esri) MAGE33_FY 2023 Male Population Age 33 (Esri) MAGE34_FY 2023 Male Population Age 34 (Esri) MAGE35_FY 2023 Male Population Age 35 (Esri) MAGE36_FY 2023 Male Population Age 36 (Esri) MAGE37_FY 2023 Male Population Age 37 (Esri) MAGE38_FY 2023 Male Population Age 38 (Esri) MAGE39_FY 2023 Male Population Age 39 (Esri) MAGE40_FY 2023 Male Population Age 40 (Esri) MAGE41_FY 2023 Male Population Age 41 (Esri) MAGE42_FY 2023 Male Population Age 42 (Esri) MAGE43_FY 2023 Male Population Age 43 (Esri) MAGE44_FY 2023 Male Population Age 44 (Esri) MAGE45_FY 2023 Male Population Age 45 (Esri) MAGE46_FY 2023 Male Population Age 46 (Esri) MAGE47_FY 2023 Male Population Age 47 (Esri) MAGE48_FY 2023 Male Population Age 48 (Esri) MAGE49_FY 2023 Male Population Age 49 (Esri) MAGE50_FY 2023 Male Population Age 50 (Esri) MAGE51_FY 2023 Male Population Age 51 (Esri) MAGE52_FY 2023 Male Population Age 52 (Esri) MAGE53_FY 2023 Male Population Age 53 (Esri) MAGE54_FY 2023 Male Population Age 54 (Esri) MAGE55_FY 2023 Male Population Age 55 (Esri) MAGE56_FY 2023 Male Population Age 56 (Esri) MAGE57_FY 2023 Male Population Age 57 (Esri) MAGE58_FY 2023 Male Population Age 58 (Esri) MAGE59_FY 2023 Male Population Age 59 (Esri) MAGE60_FY 2023 Male Population Age 60 (Esri) MAGE61_FY 2023 Male Population Age 61 (Esri) MAGE62_FY 2023 Male Population Age 62 (Esri) MAGE63_FY 2023 Male Population Age 63 (Esri) MAGE64_FY 2023 Male Population Age 64 (Esri) MAGE65_FY 2023 Male Population Age 65 (Esri) MAGE66_FY 2023 Male Population Age 66 (Esri) MAGE67_FY 2023 Male Population Age 67 (Esri) MAGE68_FY 2023 Male Population Age 68 (Esri) MAGE69_FY 2023 Male Population Age 69 (Esri) MAGE70_FY 2023 Male Population Age 70 (Esri) MAGE71_FY 2023 Male Population Age 71 (Esri) MAGE72_FY 2023 Male Population Age 72 (Esri) MAGE73_FY 2023 Male Population Age 73 (Esri) MAGE74_FY 2023 Male Population Age 74 (Esri) MAGE75_FY 2023 Male Population Age 75 (Esri) MAGE76_FY 2023 Male Population Age 76 (Esri) MAGE77_FY 2023 Male Population Age 77 (Esri) MAGE78_FY 2023 Male Population Age 78 (Esri) MAGE79_FY 2023 Male Population Age 79 (Esri) MAGE80_FY 2023 Male Population Age 80 (Esri) MAGE81_FY 2023 Male Population Age 81 (Esri) MAGE82_FY 2023 Male Population Age 82 (Esri) MAGE83_FY 2023 Male Population Age 83 (Esri) MAGE84_FY 2023 Male Population Age 84 (Esri) FAGE0_FY 2023 Female Population Age <1 (Esri) FAGE1_FY 2023 Female Population Age 1 (Esri) FAGE2_FY 2023 Female Population Age 2 (Esri) FAGE3_FY 2023 Female Population Age 3 (Esri) FAGE4_FY 2023 Female Population Age 4 (Esri) FAGE5_FY 2023 Female Population Age 5 (Esri) FAGE6_FY 2023 Female Population Age 6 (Esri) FAGE7_FY 2023 Female Population Age 7 (Esri) FAGE8_FY 2023 Female Population Age 8 (Esri) FAGE9_FY 2023 Female Population Age 9 (Esri) FAGE10_FY 2023 Female Population Age 10 (Esri) FAGE11_FY 2023 Female Population Age 11 (Esri) FAGE12_FY 2023 Female Population Age 12 (Esri) FAGE13_FY 2023 Female Population Age 13 (Esri) FAGE14_FY 2023 Female Population Age 14 (Esri) FAGE15_FY 2023 Female Population Age 15 (Esri) FAGE16_FY 2023 Female Population Age 16 (Esri) FAGE17_FY 2023 Female Population Age 17 (Esri) FAGE18_FY 2023 Female Population Age 18 (Esri) FAGE19_FY 2023 Female Population Age 19 (Esri) FAGE20_FY 2023 Female Population Age 20 (Esri) FAGE21_FY 2023 Female Population Age 21 (Esri) FAGE22_FY 2023 Female Population Age 22 (Esri) FAGE23_FY 2023 Female Population Age 23 (Esri) FAGE24_FY 2023 Female Population Age 24 (Esri) FAGE25_FY 2023 Female Population Age 25 (Esri) FAGE26_FY 2023 Female Population Age 26 (Esri) FAGE27_FY 2023 Female Population Age 27 (Esri) FAGE28_FY 2023 Female Population Age 28 (Esri) FAGE29_FY 2023 Female Population Age 29 (Esri) FAGE30_FY 2023 Female Population Age 30 (Esri) FAGE31_FY 2023 Female Population Age 31 (Esri) FAGE32_FY 2023 Female Population Age 32 (Esri) FAGE33_FY 2023 Female Population Age 33 (Esri) FAGE34_FY 2023 Female Population Age 34 (Esri) FAGE35_FY 2023 Female Population Age 35 (Esri) FAGE36_FY 2023 Female Population Age 36 (Esri) FAGE37_FY 2023 Female Population Age 37 (Esri) FAGE38_FY 2023 Female Population Age 38 (Esri) FAGE39_FY 2023 Female Population Age 39 (Esri) FAGE40_FY 2023 Female Population Age 40 (Esri) FAGE41_FY 2023 Female Population Age 41 (Esri) FAGE42_FY 2023 Female Population Age 42 (Esri) FAGE43_FY 2023 Female Population Age 43 (Esri) FAGE44_FY 2023 Female Population Age 44 (Esri) FAGE45_FY 2023 Female Population Age 45 (Esri) FAGE46_FY 2023 Female Population Age 46 (Esri) FAGE47_FY 2023 Female Population Age 47 (Esri) FAGE48_FY 2023 Female Population Age 48 (Esri) FAGE49_FY 2023 Female Population Age 49 (Esri) FAGE50_FY 2023 Female Population Age 50 (Esri) FAGE51_FY 2023 Female Population Age 51 (Esri) FAGE52_FY 2023 Female Population Age 52 (Esri) FAGE53_FY 2023 Female Population Age 53 (Esri) FAGE54_FY 2023 Female Population Age 54 (Esri) FAGE55_FY 2023 Female Population Age 55 (Esri) FAGE56_FY 2023 Female Population Age 56 (Esri) FAGE57_FY 2023 Female Population Age 57 (Esri) FAGE58_FY 2023 Female Population Age 58 (Esri) FAGE59_FY 2023 Female Population Age 59 (Esri) FAGE60_FY 2023 Female Population Age 60 (Esri) FAGE61_FY 2023 Female Population Age 61 (Esri) FAGE62_FY 2023 Female Population Age 62 (Esri) FAGE63_FY 2023 Female Population Age 63 (Esri) FAGE64_FY 2023 Female Population Age 64 (Esri) FAGE65_FY 2023 Female Population Age 65 (Esri) FAGE66_FY 2023 Female Population Age 66 (Esri) FAGE67_FY 2023 Female Population Age 67 (Esri) FAGE68_FY 2023 Female Population Age 68 (Esri) FAGE69_FY 2023 Female Population Age 69 (Esri) FAGE70_FY 2023 Female Population Age 70 (Esri) FAGE71_FY 2023 Female Population Age 71 (Esri) FAGE72_FY 2023 Female Population Age 72 (Esri) FAGE73_FY 2023 Female Population Age 73 (Esri) FAGE74_FY 2023 Female Population Age 74 (Esri) FAGE75_FY 2023 Female Population Age 75 (Esri) FAGE76_FY 2023 Female Population Age 76 (Esri) FAGE77_FY 2023 Female Population Age 77 (Esri) FAGE78_FY 2023 Female Population Age 78 (Esri) FAGE79_FY 2023 Female Population Age 79 (Esri) FAGE80_FY 2023 Female Population Age 80 (Esri) FAGE81_FY 2023 Female Population Age 81 (Esri) FAGE82_FY 2023 Female Population Age 82 (Esri) FAGE83_FY 2023 Female Population Age 83 (Esri) FAGE84_FY 2023 Female Population Age 84 (Esri) WHITE_FY 2023 White Population (Esri) BLACK_FY 2023 Black/African American Population (Esri) AMERIND_FY 2023 American Indian/Alaska Native Population (Esri) ASIAN_FY 2023 Asian Population (Esri) PACIFIC_FY 2023 Pacific Islander Population (Esri) OTHRACE_FY 2023 Other Race Population (Esri) RACE2UP_FY 2023 Population of Two or More Races (Esri) HISPPOP_FY 2023 Hispanic Population (Esri) HISPWHT_FY 2023 Hispanic White Population (Esri) HISPBLK_FY 2023 Hispanic Black/African American Population (Esri) HISPAI_FY 2023 Hispanic American Indian/Alaska Native Population (Esri) HISPASN_FY 2023 Hispanic Asian Population (Esri) HISPPI_FY 2023 Hispanic Pacific Islander Population (Esri) HISPOTH_FY 2023 Hispanic Other Race Population (Esri) HISPMLT_FY 2023 Hispanic Population of Two or More Races (Esri) NONHISP_FY 2023 Non-Hispanic Population (Esri) NHSPWHT_FY 2023 White Non-Hispanic Population (Esri) NHSPBLK_FY 2023 Black/African American Non-Hispanic Population (Esri) NHSPAI_FY 2023 American Indian/Alaska Native Non-Hispanic Population (Esri) NHSPASN_FY 2023 Asian Non-Hispanic Population (Esri) NHSPPI_FY 2023 Pacific Islander Non-Hispanic Population (Esri) NHSPOTH_FY 2023 Other Race Non-Hispanic Population (Esri) NHSPMLT_FY 2023 Multiple Races Non-Hispanic Population (Esri) MINORITYFY 2023 Minority Population (Esri) DIVINDX_FY 2023 Diversity Index (Esri) RACEBASEFY 2023 Population by Race Base (Esri) HINC0_FY 2023 Household Income less than $15,000 (Esri) HINC15_FY 2023 Household Income $15,000-$24,999 (Esri) HINC25_FY 2023 Household Income $25,000-$34,999 (Esri) HINC35_FY 2023 Household Income $35,000-$49,999 (Esri) HINC50_FY 2023 Household Income $50,000-$74,999 (Esri) HINC75_FY 2023 Household Income $75,000-$99,999 (Esri) HINC100_FY 2023 Household Income $100,000-$149,999 (Esri) HINC150_FY 2023 Household Income $150,000-$199,999 (Esri) HINC200_FY 2023 Household Income $200,000 or greater (Esri) MEDHINC_FY 2023 Median Household Income (Esri) AVGHINC_FY 2023 Average Household Income (Esri) PCI_FY 2023 Per Capita Income (Esri) AGGINC_FY 2023 Aggregate Income (Esri) AGGHINC_FY 2023 Aggregate Household Income (Esri) HINCBASEFY 2023 Households by Income Base (Esri) A15I0_FY 2023 Household Income less than $15,000 and Householder Age 15-24 (Esri) A15I15_FY 2023 Household Income $15,000-$24,999 and Householder Age 15-24 (Esri) A15I25_FY 2023 Household Income $25,000-$34,999 and Householder Age 15-24 (Esri) A15I35_FY 2023 Household Income $35,000-$49,999 and Householder Age 15-24 (Esri) A15I50_FY 2023 Household Income $50,000-$74,999 and Householder Age 15-24 (Esri) A15I75_FY 2023 Household Income $75,000-$99,999 and Householder Age 15-24 (Esri) A15I100_FY 2023 Household Income $100,000-$149,999 and Householder Age 15-24 (Esri) A15I150_FY 2023 Household Income $150,000-$199,999 and Householder Age 15-24 (Esri) A15I200_FY 2023 Household Income $200,000+ and Householder Age 15-24 (Esri) MEDIA15_FY 2023 Median Household Income and Householder Age 15-24 (Esri) AVGIA15_FY 2023 Average Household Income and Householder Age 15-24 (Esri) IA15BASEFY 2023 Households by Income Base and Householder Age 15-24 (Esri) AGGIA15_FY 2023 Aggregate Household Income and Householder Age 15-24 (Esri) A25I0_FY 2023 Household Income less than $15,000 and Householder Age 25-34 (Esri) A25I15_FY 2023 Household Income $15,000-$24,999 and Householder Age 25-34 (Esri) A25I25_FY 2023 Household Income $25,000-$34,999 and Householder Age 25-34 (Esri) A25I35_FY 2023 Household Income $35,000-$49,999 and Householder Age 25-34 (Esri) A25I50_FY 2023 Household Income $50,000-$74,999 and Householder Age 25-34 (Esri) A25I75_FY 2023 Household Income $75,000-$99,999 and Householder Age 25-34 (Esri) A25I100_FY 2023 Household Income $100,000-$149,999 and Householder Age 25-34 (Esri) A25I150_FY 2023 Household Income $150,000-$199,999 and Householder Age 25-34 (Esri) A25I200_FY 2023 Household Income $200,000+ and Householder Age 25-34 (Esri) MEDIA25_FY 2023 Median Household Income and Householder Age 25-34 (Esri) AVGIA25_FY 2023 Average Household Income and Householder Age 25-34 (Esri) IA25BASEFY 2023 Households by Income Base and Householder Age 25-34 (Esri) AGGIA25_FY 2023 Aggregate Household Income and Householder Age 25-34 (Esri) A35I0_FY 2023 Household Income less than $15,000 and Householder Age 35-44 (Esri) A35I15_FY 2023 Household Income $15,000-$24,999 and Householder Age 35-44 (Esri) A35I25_FY 2023 Household Income $25,000-$34,999 and Householder Age 35-44 (Esri) A35I35_FY 2023 Household Income $35,000-$49,999 and Householder Age 35-44 (Esri) A35I50_FY 2023 Household Income $50,000-$74,999 and Householder Age 35-44 (Esri) A35I75_FY 2023 Household Income $75,000-$99,999 and Householder Age 35-44 (Esri) A35I100_FY 2023 Household Income $100,000-$149,999 and Householder Age 35-44 (Esri) A35I150_FY 2023 Household Income $150,000-$199,999 and Householder Age 35-44 (Esri) A35I200_FY 2023 Household Income $200,000+ and Householder Age 35-44 (Esri) MEDIA35_FY 2023 Median Household Income and Householder Age 35-44 (Esri) AVGIA35_FY 2023 Average Household Income and Householder Age 35-44 (Esri) IA35BASEFY 2023 Households by Income Base and Householder Age 35-44 (Esri) AGGIA35_FY 2023 Aggregate Household Income and Householder Age 35-44 (Esri) A45I0_FY 2023 Household Income less than $15,000 and Householder Age 45-54 (Esri) A45I15_FY 2023 Household Income $15,000-$24,999 and Householder Age 45-54 (Esri) A45I25_FY 2023 Household Income $25,000-$34,999 and Householder Age 45-54 (Esri) A45I35_FY 2023 Household Income $35,000-$49,999 and Householder Age 45-54 (Esri) A45I50_FY 2023 Household Income $50,000-$74,999 and Householder Age 45-54 (Esri) A45I75_FY 2023 Household Income $75,000-$99,999 and Householder Age 45-54 (Esri) A45I100_FY 2023 Household Income $100,000-$149,999 and Householder Age 45-54 (Esri) A45I150_FY 2023 Household Income $150,000-$199,999 and Householder Age 45-54 (Esri) A45I200_FY 2023 Household Income $200,000+ and Householder Age 45-54 (Esri) MEDIA45_FY 2023 Median Household Income and Householder Age 45-54 (Esri) AVGIA45_FY 2023 Average Household Income and Householder Age 45-54 (Esri) IA45BASEFY 2023 Households by Income Base and Householder Age 45-54 (Esri) AGGIA45_FY 2023 Aggregate Household Income and Householder Age 45-54 (Esri) A55I0_FY 2023 Household Income less than $15,000 and Householder Age 55-64 (Esri) A55I15_FY 2023 Household Income $15,000-$24,999 and Householder Age 55-64 (Esri) A55I25_FY 2023 Household Income $25,000-$34,999 and Householder Age 55-64 (Esri) A55I35_FY 2023 Household Income $35,000-$49,999 and Householder Age 55-64 (Esri) A55I50_FY 2023 Household Income $50,000-$74,999 and Householder Age 55-64 (Esri) A55I75_FY 2023 Household Income $75,000-$99,999 and Householder Age 55-64 (Esri) A55I100_FY 2023 Household Income $100,000-$149,999 and Householder Age 55-64 (Esri) A55I150_FY 2023 Household Income $150,000-$199,999 and Householder Age 55-64 (Esri) A55I200_FY 2023 Household Income $200,000+ and Householder Age 55-64 (Esri) MEDIA55_FY 2023 Median Household Income and Householder Age 55-64 (Esri) AVGIA55_FY 2023 Average Household Income and Householder Age 55-64 (Esri) IA55BASEFY 2023 Households by Income Base and Householder Age 55-64 (Esri) AGGIA55_FY 2023 Aggregate Household Income and Householder Age 55-64 (Esri) A65I0_FY 2023 Household Income less than $15,000 and Householder Age 65-74 (Esri) A65I15_FY 2023 Household Income $15,000-$24,999 and Householder Age 65-74 (Esri) A65I25_FY 2023 Household Income $25,000-$34,999 and Householder Age 65-74 (Esri) A65I35_FY 2023 Household Income $35,000-$49,999 and Householder Age 65-74 (Esri) A65I50_FY 2023 Household Income $50,000-$74,999 and Householder Age 65-74 (Esri) A65I75_FY 2023 Household Income $75,000-$99,999 and Householder Age 65-74 (Esri) A65I100_FY 2023 Household Income $100,000-$149,999 and Householder Age 65-74 (Esri) A65I150_FY 2023 Household Income $150,000-$199,999 and Householder Age 65-74 (Esri) A65I200_FY 2023 Household Income $200,000+ and Householder Age 65-74 (Esri) MEDIA65_FY 2023 Median Household Income and Householder Age 65-74 (Esri) AVGIA65_FY 2023 Average Household Income and Householder Age 65-74 (Esri) IA65BASEFY 2023 Households by Income Base and Householder Age 65-74 (Esri) AGGIA65_FY 2023 Aggregate Household Income and Householder Age 65-74 (Esri) A75I0_FY 2023 Household Income less than $15,000 and Householder Age 75+ (Esri) A75I15_FY 2023 Household Income $15,000-$24,999 and Householder Age 75+ (Esri) A75I25_FY 2023 Household Income $25,000-$34,999 and Householder Age 75+ (Esri) A75I35_FY 2023 Household Income $35,000-$49,999 and Householder Age 75+ (Esri) A75I50_FY 2023 Household Income $50,000-$74,999 and Householder Age 75+ (Esri) A75I75_FY 2023 Household Income $75,000-$99,999 and Householder Age 75+ (Esri) A75I100_FY 2023 Household Income $100,000-$149,999 and Householder Age 75+ (Esri) A75I150_FY 2023 Household Income $150,000-$199,999 and Householder Age 75+ (Esri) A75I200_FY 2023 Household Income $200,000+ and Householder Age 75+ (Esri) MEDIA75_FY 2023 Median Household Income and Householder Age 75+ (Esri) AVGIA75_FY 2023 Average Household Income and Householder Age 75+ (Esri) IA75BASEFY 2023 Households by Income Base and Householder Age 75+ (Esri) AGGIA75_FY 2023 Aggregate Household Income and Householder Age 75+ (Esri) MEDHHR_FY 2023 Median Age of Householder (Esri) MEDIA55UFY 2023 Median Household Income and Householder Age 55+ (Esri) AVGIA55UFY 2023 Average Household Income and Householder Age 55+ (Esri) IA55UBASFY 2023 Households by Income Base and Householder Age 55+ (Esri) MEDIA65UFY 2023 Median Household Income and Householder Age 65+ (Esri) AVGIA65UFY 2023 Average Household Income and Householder Age 65+ (Esri) IA65UBASFY 2023 Households by Income Base and Householder Age 65+ (Esri) VAL0_FY 2023 Home Value less than $50,000 (Esri) VAL50K_FY 2023 Home Value $50,000-$99,999 (Esri) VAL100K_FY 2023 Home Value $100,000-$149,999 (Esri) VAL150K_FY 2023 Home Value $150,000-$199,999 (Esri) VAL200K_FY 2023 Home Value $200,000-$249,999 (Esri) VAL250K_FY 2023 Home Value $250,000-$299,999 (Esri) VAL300K_FY 2023 Home Value $300,000-$399,999 (Esri) VAL400K_FY 2023 Home Value $400,000-$499,999 (Esri) VAL500K_FY 2023 Home Value $500,000-$749,999 (Esri) VAL750K_FY 2023 Home Value $750,000-$999,999 (Esri) VAL1M_FY 2023 Home Value $1,000,000-$1,499,999 (Esri) VAL1PT5MFY 2023 Home Value $1,500,000-$1,999,999 (Esri) VAL2M_FY 2023 Home Value $2,000,000 or greater (Esri) MEDVAL_FY 2023 Median Home Value (Esri) AVGVAL_FY 2023 Average Home Value (Esri) VALBASE_FY 2023 Owner Occupied Housing Units by Value Base (Esri) TOTPOP10 2010 Total Population (U.S. Census) POPDENS10 2010 Population Density (Pop per Square Mile) (U.S. Census) MALES10 2010 Male Population (U.S. Census) FEMALES10 2010 Female Population (U.S. Census) HHPOP10 2010 Population in Households (U.S. Census) FAMPOP10 2010 Population in Families (U.S. Census) POPGRW0010 2000-2010 Population Annual Compound Growth Rate (U.S. Census) URBARPOP10 2010 Population Inside Urbanized Areas (U.S. Census) URBCLPOP10 2010 Population Inside Urban Clusters (U.S. Census) RURALPOP10 2010 Rural Population (U.S. Census) URBANPOP10 2010 Urban Population (U.S. Census) URPOPBAS10 2010 Urban/Rural Population Base (U.S. Census) POP0C10 2010 Total Population Age 0-4 (U.S. Census) POP5C10 2010 Total Population Age 5-9 (U.S. Census) POP10C10 2010 Total Population Age 10-14 (U.S. Census) POP15C10 2010 Total Population Age 15-19 (U.S. Census) POP20C10 2010 Total Population Age 20-24 (U.S. Census) POP25C10 2010 Total Population Age 25-29 (U.S. Census) POP30C10 2010 Total Population Age 30-34 (U.S. Census) POP35C10 2010 Total Population Age 35-39 (U.S. Census) POP40C10 2010 Total Population Age 40-44 (U.S. Census) POP45C10 2010 Total Population Age 45-49 (U.S. Census) POP50C10 2010 Total Population Age 50-54 (U.S. Census) POP55C10 2010 Total Population Age 55-59 (U.S. Census) POP60C10 2010 Total Population Age 60-64 (U.S. Census) POP65C10 2010 Total Population Age 65-69 (U.S. Census) POP70C10 2010 Total Population Age 70-74 (U.S. Census) POP75C10 2010 Total Population Age 75-79 (U.S. Census) POP80C10 2010 Total Population Age 80-84 (U.S. Census) POP85C10 2010 Total Population Age 85+ (U.S. Census) ADULTS10 2010 Population Age 18+ (U.S. Census) POP21UP10 2010 Total Population Age 21+ (U.S. Census) MEDAGE10 2010 Median Age (U.S. Census) AGEBASE10 2010 Base for Total Population by 5-Year Age Ranges (U.S. Census) MALE0C10 2010 Males Age 0-4 (U.S. Census) MALE5C10 2010 Males Age 5-9 (U.S. Census) MALE10C10 2010 Males Age 10-14 (U.S. Census) MALE15C10 2010 Males Age 15-19 (U.S. Census) MALE20C10 2010 Males Age 20-24 (U.S. Census) MALE25C10 2010 Males Age 25-29 (U.S. Census) MALE30C10 2010 Males Age 30-34 (U.S. Census) MALE35C10 2010 Males Age 35-39 (U.S. Census) MALE40C10 2010 Males Age 40-44 (U.S. Census) MALE45C10 2010 Males Age 45-49 (U.S. Census) MALE50C10 2010 Males Age 50-54 (U.S. Census) MALE55C10 2010 Males Age 55-59 (U.S. Census) MALE60C10 2010 Males Age 60-64 (U.S. Census) MALE65C10 2010 Males Age 65-69 (U.S. Census) MALE70C10 2010 Males Age 70-74 (U.S. Census) MALE75C10 2010 Males Age 75-79 (U.S. Census) MALE80C10 2010 Males Age 80-84 (U.S. Census) MALE85C10 2010 Males Age 85+ (U.S. Census) MAL18UP10 2010 Males Age 18+ (U.S. Census) MAL21UP10 2010 Males Age 21+ (U.S. Census) MEDMAGE10 2010 Median Male Age (U.S. Census) MAGEBASE10 2010 Base for Male Population by 5-Year Age Ranges (U.S. Census) FEM0C10 2010 Females Age 0-4 (U.S. Census) FEM5C10 2010 Females Age 5-9 (U.S. Census) FEM10C10 2010 Females Age 10-14 (U.S. Census) FEM15C10 2010 Females Age 15-19 (U.S. Census) FEM20C10 2010 Females Age 20-24 (U.S. Census) FEM25C10 2010 Females Age 25-29 (U.S. Census) FEM30C10 2010 Females Age 30-34 (U.S. Census) FEM35C10 2010 Females Age 35-39 (U.S. Census) FEM40C10 2010 Females Age 40-44 (U.S. Census) FEM45C10 2010 Females Age 45-49 (U.S. Census) FEM50C10 2010 Females Age 50-54 (U.S. Census) FEM55C10 2010 Females Age 55-59 (U.S. Census) FEM60C10 2010 Females Age 60-64 (U.S. Census) FEM65C10 2010 Females Age 65-69 (U.S. Census) FEM70C10 2010 Females Age 70-74 (U.S. Census) FEM75C10 2010 Females Age 75-79 (U.S. Census) FEM80C10 2010 Females Age 80-84 (U.S. Census) FEM85C10 2010 Females Age 85+ (U.S. Census) FEM18UP10 2010 Females Age 18+ (U.S. Census) FEM21UP10 2010 Females Age 21+ (U.S. Census) MEDFAGE10 2010 Median Female Age (U.S. Census) FAGEBASE10 2010 Base for Female Population by 5-Year Age Ranges (U.S. Census) AGE0C10 2010 Total Population Age <1 (U.S. Census) AGE1C10 2010 Total Population Age 1 (U.S. Census) AGE2C10 2010 Total Population Age 2 (U.S. Census) AGE3C10 2010 Total Population Age 3 (U.S. Census) AGE4C10 2010 Total Population Age 4 (U.S. Census) AGE5C10 2010 Total Population Age 5 (U.S. Census) AGE6C10 2010 Total Population Age 6 (U.S. Census) AGE7C10 2010 Total Population Age 7 (U.S. Census) AGE8C10 2010 Total Population Age 8 (U.S. Census) AGE9C10 2010 Total Population Age 9 (U.S. Census) AGE10C10 2010 Total Population Age 10 (U.S. Census) AGE11C10 2010 Total Population Age 11 (U.S. Census) AGE12C10 2010 Total Population Age 12 (U.S. Census) AGE13C10 2010 Total Population Age 13 (U.S. Census) AGE14C10 2010 Total Population Age 14 (U.S. Census) AGE15C10 2010 Total Population Age 15 (U.S. Census) AGE16C10 2010 Total Population Age 16 (U.S. Census) AGE17C10 2010 Total Population Age 17 (U.S. Census) AGE18C10 2010 Total Population Age 18 (U.S. Census) AGE19C10 2010 Total Population Age 19 (U.S. Census) AGE20C10 2010 Total Population Age 20 (U.S. Census) AGE21C10 2010 Total Population Age 21 (U.S. Census) MAGE0C10 2010 Males Age <1 (U.S. Census) MAGE1C10 2010 Males Age 1 (U.S. Census) MAGE2C10 2010 Males Age 2 (U.S. Census) MAGE3C10 2010 Males Age 3 (U.S. Census) MAGE4C10 2010 Males Age 4 (U.S. Census) MAGE5C10 2010 Males Age 5 (U.S. Census) MAGE6C10 2010 Males Age 6 (U.S. Census) MAGE7C10 2010 Males Age 7 (U.S. Census) MAGE8C10 2010 Males Age 8 (U.S. Census) MAGE9C10 2010 Males Age 9 (U.S. Census) MAGE10C10 2010 Males Age 10 (U.S. Census) MAGE11C10 2010 Males Age 11 (U.S. Census) MAGE12C10 2010 Males Age 12 (U.S. Census) MAGE13C10 2010 Males Age 13 (U.S. Census) MAGE14C10 2010 Males Age 14 (U.S. Census) MAGE15C10 2010 Males Age 15 (U.S. Census) MAGE16C10 2010 Males Age 16 (U.S. Census) MAGE17C10 2010 Males Age 17 (U.S. Census) MAGE18C10 2010 Males Age 18 (U.S. Census) MAGE19C10 2010 Males Age 19 (U.S. Census) MAGE20C10 2010 Males Age 20 (U.S. Census) MAGE21C10 2010 Males Age 21 (U.S. Census) FAGE0C10 2010 Females Age <1 (U.S. Census) FAGE1C10 2010 Females Age 1 (U.S. Census) FAGE2C10 2010 Females Age 2 (U.S. Census) FAGE3C10 2010 Females Age 3 (U.S. Census) FAGE4C10 2010 Females Age 4 (U.S. Census) FAGE5C10 2010 Females Age 5 (U.S. Census) FAGE6C10 2010 Females Age 6 (U.S. Census) FAGE7C10 2010 Females Age 7 (U.S. Census) FAGE8C10 2010 Females Age 8 (U.S. Census) FAGE9C10 2010 Females Age 9 (U.S. Census) FAGE10C10 2010 Females Age 10 (U.S. Census) FAGE11C10 2010 Females Age 11 (U.S. Census) FAGE12C10 2010 Females Age 12 (U.S. Census) FAGE13C10 2010 Females Age 13 (U.S. Census) FAGE14C10 2010 Females Age 14 (U.S. Census) FAGE15C10 2010 Females Age 15 (U.S. Census) FAGE16C10 2010 Females Age 16 (U.S. Census) FAGE17C10 2010 Females Age 17 (U.S. Census) FAGE18C10 2010 Females Age 18 (U.S. Census) FAGE19C10 2010 Females Age 19 (U.S. Census) FAGE20C10 2010 Females Age 20 (U.S. Census) FAGE21C10 2010 Females Age 21 (U.S. Census) WHITE10 2010 White Population (U.S. Census) BLACK10 2010 Black/African American Population (U.S. Census) AMERIND10 2010 American Indian/Alaska Native Population (U.S. Census) ASIAN10 2010 Asian Population (U.S. Census) PACIFIC10 2010 Pacific Islander Population (U.S. Census) OTHRACE10 2010 Other Race Population (U.S. Census) H1RACE10 2010 Hispanic Population Reporting One Race (U.S. Census) RACE2UP10 2010 Population of Two or More Races (U.S. Census) HISPPOP10 2010 Hispanic Population (U.S. Census) HWHITE10 2010 Hispanic White Population (U.S. Census) HBLACK10 2010 Hispanic Black/African American Population (U.S. Census) HAMERIND10 2010 Hispanic American Indian/Alaska Native Population (U.S. Census) HASIAN10 2010 Hispanic Asian Population (U.S. Census) HPACIFIC10 2010 Hispanic Pacific Islander Population (U.S. Census) HOTHRACE10 2010 Hispanic Other Race Population (U.S. Census) HRACE2UP10 2010 Hispanic Population Reporting Two or More Races (U.S. Census) WHTNHISP10 2010 White Non-Hispanic Population (U.S. Census) MINORITY10 2010 Minority Population (U.S. Census) DIVINDX10 2010 Diversity Index (U.S. Census) HPWHTHHR10 2010 Population Living in Households with a White Householder (U.S. Census) HPBLKHHR10 2010 Population Living in Households with a Black/African American Householder (U.S. Census) HPAIHHR10 2010 Population Living in Households with an American Indian/Alaska Native Householder (U.S. Census) HPASNHHR10 2010 Population Living in Households with an Asian Householder (U.S. Census) HPPIHHR10 2010 Population Living in Households with a Pacific Islander Householder (U.S. Census) HPOTHHHR10 2010 Population Living in Households with an Other Race Householder (U.S. Census) HPMLTHHR10 2010 Population Living in Households with a Multiple Races Householder (U.S. Census) HPHSPHHR10 2010 Population Living in Households with a Hispanic Householder (U.S. Census) HRACEBAS10 2010 Base for Hispanic Population by Race (U.S. Census) RACEBASE10 2010 Base for Population by Race (U.S. Census) POP1RACE10 2010 Population Reporting One Race (U.S. Census) U18RBASE10 2010 Base for Population <18 by Race (U.S. Census) ADRACBAS10 2010 Base for Population Age 18+ by Race (U.S. Census) AD1RACE10 2010 Population Age 18+ Reporting One Race (U.S. Census) HADRBASE10 2010 Base for Hispanic Population Age 18+ by Race (U.S. Census) HAD1RACE10 2010 Hispanic Population Age 18+ Reporting One Race (U.S. Census) MALHHRFH10 2010 Male Householder Living in Family Households (U.S. Census) FEMHHRFH10 2010 Female Householder Living in Family Households (U.S. Census) SPOUSEFH10 2010 Spouse Living in Family Households (U.S. Census) BIOLCHFH10 2010 Biological Child Living in Family Households (U.S. Census) ADPTCHFH10 2010 Adopted Child Living in Family Households (U.S. Census) STEPCHFH10 2010 Stepchild Living in Family Households (U.S. Census) GRNDCHFH10 2010 Grandchild Living in Family Households (U.S. Census) BROSISFH10 2010 Brother or Sister Living in Family Households (U.S. Census) PARENTFH10 2010 Parent Living in Family Households (U.S. Census) PINLAWFH10 2010 Parent-in-law Living in Family Households (U.S. Census) SDINLAWF10 2010 Son-in-law or Daughter-in-law Living in Family Households (U.S. Census) OTHRELFH10 2010 Other Relative Living in Family Households (U.S. Census) NONRELFH10 2010 Nonrelative Living in Family Households (U.S. Census) MHHR1NF10 2010 Male Householder Living Alone in Nonfamily Households (U.S. Census) MHHR2NF10 2010 Male Householder Not Living Alone in Nonfamily Households (U.S. Census) FHHR1NF10 2010 Female Householder Living Alone in Nonfamily Households (U.S. Census) FHHR2NF10 2010 Female Householder Not Living Alone in Nonfamily Households (U.S. Census) NONRELNF10 2010 Nonrelative Living in Nonfamily Households (U.S. Census) P65FAMHH10 2010 Population Age 65+ Living in Family Households (U.S. Census) P65MHHRF10 2010 Male Householder Age 65+ Living in Family Households (U.S. Census) P65FHHRF10 2010 Female Householder Age 65+ Living in Family Households (U.S. Census) P65SPOUS10 2010 Spouse Age 65+ Living in Family Households (U.S. Census) P65PARNT10 2010 Parent Age 65+ Living in Family Households (U.S. Census) P65PINLW10 2010 Parent-in-law Age 65+ Living in Family Households (U.S. Census) P65OREL10 2010 Other Relative Age 65+ Living in Family Households (U.S. Census) P65NRELF10 2010 Nonrelative Age 65+ Living in Family Households (U.S. Census) P65NFHH10 2010 Population Age 65+ Living in Nonfamily Households (U.S. Census) P65MH1NF10 2010 Male Householder Age 65+ Living Alone in Nonfamily Households (U.S. Census) P65MH2NF10 2010 Male Householder Age 65+ Not Living Alone in Nonfamily Households (U.S. Census) P65FH1NF10 2010 Female Householder Age 65+ Living Alone in Nonfamily Households (U.S. Census) P65FH2NF10 2010 Female Householder Age 65+ Not Living Alone in Nonfamily Households (U.S. Census) P65NRLNF10 2010 Nonrelative Age 65+ Living in Nonfamily Households (U.S. Census) GQPOP10 2010 Population in Group Quarters (U.S. Census) GQINST10 2010 Institutionalized Population in Group Quarters (U.S. Census) GQPRISON10 2010 Institutionalized Population in Adult Correctional Facilities (U.S. Census) GQJUV10 2010 Institutionalized Population in Juvenile Facilities (U.S. Census) GQNURS10 2010 Institutionalized Population in Nursing Facilities (U.S. Census) GQOTINST10 2010 Institutionalized Population in Other Institutional (U.S. Census) GQNINST10 2010 Noninstitutionalized Population in Group Quarters (U.S. Census) GQCOLL10 2010 Noninstitutionalized Population in College Student Housing (U.S. Census) GQMIL10 2010 Noninstitutionalized Population in Military Quarters (U.S. Census) GQONINST10 2010 Noninstitutionalized Population in Other Noninstitutional (U.S. Census) POPU18GQ10 2010 Population Age <18 in Group Quarters (U.S. Census) U18INST10 2010 Institutionalized Population Age <18 in Group Quarters (U.S. Census) U18NINST10 2010 Noninstitutionalized Population Age <18 in Group Quarters (U.S. Census) POP65GQ10 2010 Population Age 65+ in Group Quarters (U.S. Census) P65INST10 2010 Institutionalized Population Age 65+ in Group Quarters (U.S. Census) P65NINST10 2010 Noninstitutionalized Population Age 65+ in Group Quarters: (U.S. Census) TOTHH10 2010 Total Households (U.S. Census) HHU18C10 2010 Households with Population Age <18 (U.S. Census) MLTGENHH10 2010 Multigenerational Households (Three or More Generations) (U.S. Census) UNMARRMF10 2010 Unmarried-partner Households: Male and Female (U.S. Census) UNMARRSS10 2010 Unmarried-partner Households: Same Sex (U.S. Census) FAMSENR10 2010 Family Households with Population Age 65+ (U.S. Census) HHGRW0010 2000-2010 Households Annual Compound Growth Rate (U.S. Census) FAMGRW0010 2000-2010 Families Annual Compound Growth Rate (U.S. Census) FAMHH10 2010 Total Family Households (U.S. Census) HWFAMHH10 2010 Husband-wife Family Households (U.S. Census) OTHFAMHH10 2010 Other Family Households (U.S. Census) OFAMMHHR10 2010 Other Family Households with a Male Householder (U.S. Census) OFAMFHHR10 2010 Other Family Households with a Female Householder (U.S. Census) NONFAMHH10 2010 Total Nonfamily Households (U.S. Census) NONFAM1P10 2010 Nonfamily Households with a Householder Living Alone (U.S. Census) NF2PMHHR10 2010 Nonfamily 2+ Person Households with a Male Householder (U.S. Census) NF2PFHHR10 2010 Nonfamily 2+ Person Households with a Female Householder (U.S. Census) POPFAMHH10 2010 Population Living in Family Households (U.S. Census) POPHWFAM10 2010 Population Living in Husband-wife Family Households (U.S. Census) POPOFAMM10 2010 Population Living in Other Family Households with a Male Householder and No Spouse (U.S. Census) POPOFAMF10 2010 Population Living in Other Family Households with a Female Householder and No Spouse (U.S. Census) POPNFHH10 2010 Total Population Living in Nonfamily Households (U.S. Census) POP1PNF10 2010 Population Living Alone in Nonfamily Households (U.S. Census) POP2PNFM10 2010 Population Not Living Alone in Nonfamily Households with a Male Householder (U.S. Census) POP2PNFF10 2010 Population Not Living Alone in Nonfamily Households with a Female Householder (U.S. Census) AVGHHSZ10 2010 Average Household Size (U.S. Census) AVGHSZOO10 2010 Average Household Size of Owner-occupied Housing Units (U.S. Census) AVGHSZRO10 2010 Average Household Size of Renter-occupied Housing Units (U.S. Census) AVGFMSZ10 2010 Average Family Size (U.S. Census) AVGNFMSZ10 2010 Average Nonfamily Size (U.S. Census) AVGHSZWH10 2010 Average Household Size of Households with a White Householder (U.S. Census) AVGHSZBL10 2010 Average Household Size of Households with a Black/African American Householder (U.S. Census) AVGHSZAI10 2010 Average Household Size of Households with an American Indian/Alaska Native Householder (U.S. Census) AVGHSZAS10 2010 Average Household Size of Households with an Asian Householder (U.S. Census) AVGHSZPI10 2010 Average Household Size of Households with a Pacific Islander Householder (U.S. Census) AVGHSZOT10 2010 Average Household Size of Households with an Other Race Householder (U.S. Census) AVGHSZ2R10 2010 Average Household Size of Households with a Multiple Races Householder (U.S. Census) AVGHSZHS10 2010 Average Household Size of Households with a Hispanic Householder (U.S. Census) FMSZBASE10 2010 Base for Family Households by Size (U.S. Census) HHWHTHHR10 2010 Households with a White Householder (U.S. Census) HHBLKHHR10 2010 Households with a Black/African American Householder (U.S. Census) HHAIHHR10 2010 Households with an American Indian/Alaska Native Householder (U.S. Census) HHASNHHR10 2010 Households with an Asian Householder (U.S. Census) HHPIHHR10 2010 Households with a Pacific Islander Householder (U.S. Census) HHOTHHHR10 2010 Households with an Other Race Householder (U.S. Census) HHMLTHHR10 2010 Households with a Multiple Races Householder (U.S. Census) HHHSPHHR10 2010 Households with a Hispanic Householder (U.S. Census) FAMHHWHT10 2010 Family Households with a White Householder (U.S. Census) FAMHHBLK10 2010 Family Households with a Black/African American Householder (U.S. Census) FAMHHAI10 2010 Family Households with an American Indian/Alaska Native Householder (U.S. Census) FAMHHASN10 2010 Family Households with an Asian Householder (U.S. Census) FAMHHPI10 2010 Family Households with a Pacific Islander Householder (U.S. Census) FAMHHOTH10 2010 Family Households with an Other Race Householder (U.S. Census) FAMHHMLT10 2010 Family Households with a Multiple Races Householder (U.S. Census) FAMHHWNH10 2010 Family Households with a White Non-Hispanic Householder (U.S. Census) FAMHHHSP10 2010 Family Households with a Hispanic Householder (U.S. Census) HWFAMWHT10 2010 Husband-wife Family Households with a White Householder (U.S. Census) HWFAMBLK10 2010 Husband-wife Family Households with a Black/African American Householder (U.S. Census) HWFAMAI10 2010 Husband-wife Family Households with an American Indian/Alaska Native Householder (U.S. Census) HWFAMASN10 2010 Husband-wife Family Households with an Asian Householder (U.S. Census) HWFAMPI10 2010 Husband-wife Family Households with a Pacific Islander Householder (U.S. Census) HWFAMOTH10 2010 Husband-wife Family Households with an Other Race Householder (U.S. Census) HWFAMMLT10 2010 Husband-wife Family Households with a Multiple Races Householder (U.S. Census) HWFAMWNH10 2010 Husband-wife Family Households with a White Non-Hispanic Householder (U.S. Census) HWFAMHSP10 2010 Husband-wife Family Households with a Hispanic Householder (U.S. Census) OFAMWHT10 2010 Other Family Households with a White Householder (U.S. Census) OFAMBLK10 2010 Other Family Households with a Black/African American Householder (U.S. Census) OFAMAI10 2010 Other Family Households with an American Indian/Alaska Native Householder (U.S. Census) OFAMASN10 2010 Other Family Households with an Asian Householder (U.S. Census) OFAMPI10 2010 Other Family Households with a Pacific Islander Householder (U.S. Census) OFAMOTH10 2010 Other Family Households with an Other Race Householder (U.S. Census) OFAMMLT10 2010 Other Family Households with a Multiple Races Householder (U.S. Census) OFAMWNH10 2010 Other Family Households with a White Non-Hispanic Householder (U.S. Census) OFAMHSP10 2010 Other Family Households with a Hispanic Householder (U.S. Census) NFAMWHT10 2010 Nonfamily Households with a White Householder (U.S. Census) NFAMBLK10 2010 Nonfamily Households with a Black/African American Householder (U.S. Census) NFAMAI10 2010 Nonfamily Households with an American Indian/Alaska Native Householder (U.S. Census) NFAMASN10 2010 Nonfamily Households with an Asian Householder (U.S. Census) NFAMPI10 2010 Nonfamily Households with a Pacific Islander Householder (U.S. Census) NFAMOTH10 2010 Nonfamily Households with an Other Race Householder (U.S. Census) NFAMMLT10 2010 Nonfamily Households with a Multiple Races Householder (U.S. Census) NFAMWNH10 2010 Nonfamily Households with a White Non-Hispanic Householder (U.S. Census) NFAMHSP10 2010 Nonfamily Households with a Hispanic Householder (U.S. Census) HHRACBAS10 2010 Base for Households by Race of Householder (U.S. Census) HWRACBAS10 2010 Base for Husband-wife Family Households by Race of Householder (U.S. Census) FMRACBAS10 2010 Base for Family Households by Race of Householder (U.S. Census) OFRACBAS10 2010 Base for Other Family Households by Race of Householder (U.S. Census) NFRACBAS10 2010 Base for Nonfamily Households by Race of Householder (U.S. Census) FAM2PERS10 2010 Family Households with 2 People (U.S. Census) FAM3PERS10 2010 Family Households with 3 People (U.S. Census) FAM4PERS10 2010 Family Households with 4 People (U.S. Census) FAM5PERS10 2010 Family Households with 5 People (U.S. Census) FAM6PERS10 2010 Family Households with 6 People (U.S. Census) FAM7PERS10 2010 Family Households with 7+ People (U.S. Census) NF1PERS10 2010 Nonfamily Households with 1 Person (U.S. Census) NF2PERS10 2010 Nonfamily Households with 2 People (U.S. Census) NF3PERS10 2010 Nonfamily Households with 3 People (U.S. Census) NF4PERS10 2010 Nonfamily Households with 4 People (U.S. Census) NF5PERS10 2010 Nonfamily Households with 5 People (U.S. Census) NF6PERS10 2010 Nonfamily Households with 6 People (U.S. Census) NF7PERS10 2010 Nonfamily Households with 7+ People (U.S. Census) NFSZBASE10 2010 Base for Nonfamily Households by Size (U.S. Census) FMHHR15C10 2010 Family Households with a Householder Age 15-24 (U.S. Census) FMHHR25C10 2010 Family Households with a Householder Age 25-34 (U.S. Census) FMHHR35C10 2010 Family Households with a Householder Age 35-44 (U.S. Census) FMHHR45C10 2010 Family Households with a Householder Age 45-54 (U.S. Census) FMHHR55C10 2010 Family Households with a Householder Age 55-64 (U.S. Census) FMHHR65C10 2010 Family Households with a Householder Age 65-74 (U.S. Census) FMHHR75C10 2010 Family Households with a Householder Age 75-84 (U.S. Census) FMHHR85C10 2010 Family Households with a Householder Age 85+ (U.S. Census) NFHHR15C10 2010 Nonfamily Households with a Householder Age 15-24 (U.S. Census) NFHHR25C10 2010 Nonfamily Households with a Householder Age 25-34 (U.S. Census) NFHHR35C10 2010 Nonfamily Households with a Householder Age 35-44 (U.S. Census) NFHHR45C10 2010 Nonfamily Households with a Householder Age 45-54 (U.S. Census) NFHHR55C10 2010 Nonfamily Households with a Householder Age 55-64 (U.S. Census) NFHHR65C10 2010 Nonfamily Households with a Householder Age 65-74 (U.S. Census) NFHHR75C10 2010 Nonfamily Households with a Householder Age 75-84 (U.S. Census) NFHHR85C10 2010 Nonfamily Households with a Householder Age 85+ (U.S. Census) FMAGEBAS10 2010 Base for Family Households by Age of Householder (U.S. Census) NFAGEBAS10 2010 Base for Nonfamily Households by Age of Householder (U.S. Census) HU18HWF10 2010 Husband-wife Family Households with Population Age <18 (U.S. Census) HU18OFM10 2010 Other Family Households with a Male Householder and Population Age <18 (U.S. Census) HU18OFF10 2010 Other Family Households with a Female Householder and Population Age <18 (U.S. Census) HU18NFM10 2010 Nonfamily Households with a Male Householder and Population Age <18 (U.S. Census) HU18NFF10 2010 Nonfamily Households with a Female Householder and Population Age <18 (U.S. Census) HWFU18O10 2010 Husband-wife Families with Own Children Age <18 (U.S. Census) HWFU6O10 2010 Husband-wife Families with Own Children Age <6 Only (U.S. Census) HWF2AGEO10 2010 Husband-wife Families with Own Children Age <6 and 6-17 (U.S. Census) HWF617O10 2010 Husband-wife Families with Own Children Age 6-17 Only (U.S. Census) OFU18O10 2010 Other Family Households with Own Children Age <18 (U.S. Census) OFU6O10 2010 Other Family Households with Own Children Age <6 Only (U.S. Census) OF2AGEO10 2010 Other Family Households with Own Children Age <6 and 6-17 (U.S. Census) OF617O10 2010 Other Family Households with Own Children Age 6-17 Only (U.S. Census) OFMU18O10 2010 Other Family Households with a Male Householder and Own Children Age <18 (U.S. Census) OFMU6O10 2010 Other Family Households with a Male Householder and Own Children Age <6 Only (U.S. Census) OFM2AGEO10 2010 Other Family Households with a Male Householder and Own Children Age <6 and 6-17 (U.S. Census) OFM617O10 2010 Other Family Households with a Male Householder and Own Children Age 6-17 Only (U.S. Census) OFFU18O10 2010 Other Family Households with a Female Householder and Own Children Age <18 (U.S. Census) OFFU6O10 2010 Other Family Households with a Female Householder and Own Children Age <6 Only (U.S. Census) OFF2AGEO10 2010 Other Family Households with a Female Householder and Own Children Age <6 and 6-17 (U.S. Census) OFF617O10 2010 Other Family Households with a Female Householder and Own Children Age 6-17 Only (U.S. Census) FWHTU18O10 2010 Family Households with a White Householder and Own Children Age <18 (U.S. Census) FBLKU18O10 2010 Family Households with a Black/African American Householder and Own Children Age <18 (U.S. Census) FAIU18O10 2010 Family Households with an American Indian/Alaska Native Householder and Own Children Age <18 (U.S. Census) FASNU18O10 2010 Family Households with an Asian Householder and Own Children Age <18 (U.S. Census) FPIU18O10 2010 Family Households with a Pacific Islander Householder and Own Children Age <18 (U.S. Census) FOTHU18O10 2010 Family Households with an Other Race Householder and Own Children Age <18 (U.S. Census) FMLTU18O10 2010 Family Households with a Multiple Races Householder and Own Children Age <18 (U.S. Census) FWNHU18O10 2010 Family Households with a White Non-Hispanic Householder and Own Children Age <18 (U.S. Census) FHSPU18O10 2010 Family Households with a Hispanic Householder and Own Children Age <18 (U.S. Census) HWFU18R10 2010 Husband-wife Families with Related Children Age <18 (U.S. Census) HWFU6R10 2010 Husband-wife Families with Related Children Age <6 Only (U.S. Census) HWF2AGER10 2010 Husband-wife Families with Related Children Age <6 and 6-17 (U.S. Census) HWF617R10 2010 Husband-wife Families with Related Children Age 6-17 Only (U.S. Census) OFU18R10 2010 Other Family Households with Related Children Age <18 (U.S. Census) OFU6R10 2010 Other Family Households with Related Children Age <6 Only (U.S. Census) OF2AGER10 2010 Other Family Households with Related Children Age <6 and 6-17 (U.S. Census) OF617R10 2010 Other Family Households with Related Children Age 6-17 Only (U.S. Census) OFMU18R10 2010 Other Family Households with a Male Householder and Related Children Age <18 (U.S. Census) OFMU6R10 2010 Other Family Households with a Male Householder and Related Children Age <6 Only (U.S. Census) OFM2AGER10 2010 Other Family Households with a Male Householder and Related Children Age <6 and 6-17 (U.S. Census) OFM617R10 2010 Other Family Households with a Male Householder and Related Children Age 6-17 Only (U.S. Census) OFFU18R10 2010 Other Family Households with a Female Householder and Related Children Age <18 (U.S. Census) OFFU6R10 2010 Other Family Households with a Female Householder and Related Children Age <6 Only (U.S. Census) OFF2AGER10 2010 Other Family Households with a Female Householder and Related Children Age <6 and 6-17 (U.S. Census) OFF617R10 2010 Other Family Households with a Female Householder and Related Children Age 6-17 Only (U.S. Census) FWHTU18R10 2010 Family Households with a White Householder and Related Children Age <18 (U.S. Census) FBLKU18R10 2010 Family Households with a Black/African American Householder and Related Children Age <18 (U.S. Census) FAIU18R10 2010 Family Households with an American Indian/Alaska Native Householder and Related Children Age <18 (U.S. Census) FASNU18R10 2010 Family Households with an Asian Householder and Related Children Age <18 (U.S. Census) FPIU18R10 2010 Family Households with a Pacific Islander Householder and Related Children Age <18 (U.S. Census) FOTHU18R10 2010 Family Households with an Other Race Householder and Related Children Age <18 (U.S. Census) FMLTU18R10 2010 Family Households with a Multiple Races Householder and Related Children Age <18 (U.S. Census) FWNHU18R10 2010 Family Households with a White Non-Hispanic Householder and Related Children Age <18 (U.S. Census) FHSPU18R10 2010 Family Households with a Hispanic Householder and Related Children Age <18 (U.S. Census) HHNU18C10 2010 Households with No Population Age <18 (U.S. Census) HNU18HWF10 2010 Husband-wife Family Households with No Population Age <18 (U.S. Census) HNU18OFM10 2010 Other Family Households with a Male Householder and No Population Age <18 (U.S. Census) HNU18OFF10 2010 Other Family Households with a Female Householder and No Population Age <18 (U.S. Census) HNU18NFM10 2010 Nonfamily Households with a Male Householder and No Population Age <18 (U.S. Census) HNU18NFF10 2010 Nonfamily Households with a Female Householder and No Population Age <18 (U.S. Census) TOTHU10 2010 Total Housing Units (U.S. Census) OWNER10 2010 Owner-occupied Housing Units (U.S. Census) OOMORT10 2010 Owner-occupied Housing Units with a Mortgage/loan (U.S. Census) OONOMORT10 2010 Owner-occupied Housing Units Owned Free and Clear (U.S. Census) RENTER10 2010 Renter-occupied Housing Units (U.S. Census) POPOWN10 2010 Population Living in Owner-occupied Housing Units (U.S. Census) POPRENT10 2010 Population Living in Renter-occupied Housing Units (U.S. Census) POPOWNM10 2010 Population Living in Occupied Housing units Owned with a Mortgage/Loan (U.S. Census) POPOWNNM10 2010 Population Living in Occupied Housing Units Owned Free and Clear (U.S. Census) URHUBASE10 2010 Urban/Rural Housing Unit Base (U.S. Census) URBARHU10 2010 Housing Units Inside Urbanized Areas (U.S. Census) URBCLHU10 2010 Housing Units Inside Urban Clusters (U.S. Census) RURALHU10 2010 Rural Housing Units (U.S. Census) URBANHU10 2010 Urban Housing Units (U.S. Census) OOWHTHHR10 2010 Owner-occupied Housing Units with a White Householder (U.S. Census) OOBLKHHR10 2010 Owner-occupied Housing Units with a Black/African American Householder (U.S. Census) OOAIHHR10 2010 Owner-occupied Housing Units with an American Indian/Alaska Native Householder (U.S. Census) OOASNHHR10 2010 Owner-occupied Housing Units with an Asian Householder (U.S. Census) OOPIHHR10 2010 Owner-occupied Housing Units with a Pacific Islander Householder (U.S. Census) OOOTHHHR10 2010 Owner-occupied Housing Units with an Other Race Householder (U.S. Census) OOMLTHHR10 2010 Owner-occupied Housing Units with a Multiple Races Householder (U.S. Census) OOHSPHHR10 2010 Owner-occupied Housing Units with a Hispanic Householder (U.S. Census) ROWHTHHR10 2010 Renter-occupied Housing Units with a White Householder (U.S. Census) ROBLKHHR10 2010 Renter-occupied Housing Units with a Black/African American Householder (U.S. Census) ROAIHHR10 2010 Renter-occupied Housing Units with an American Indian/Alaska Native Householder (U.S. Census) ROASNHHR10 2010 Renter-occupied Housing Units with an Asian Householder (U.S. Census) ROPIHHR10 2010 Renter-occupied Housing Units with a Pacific Islander Householder (U.S. Census) ROOTHHHR10 2010 Renter-occupied Housing Units with an Other Race Householder (U.S. Census) ROMLTHHR10 2010 Renter-occupied Housing Units with a Multiple Races Householder (U.S. Census) ROHSPHHR10 2010 Renter-occupied Housing Units with a Hispanic Householder (U.S. Census) OORACBAS10 2010 Base for Owner-occupied Housing Units by Race of Householder (U.S. Census) RORACBAS10 2010 Base for Renter-occupied Housing Units by Race of Householder (U.S. Census) OWN1PERS10 2010 Owner-occupied Housing Units with 1 Person (U.S. Census) OWN2PERS10 2010 Owner-occupied Housing Units with 2 People (U.S. Census) OWN3PERS10 2010 Owner-occupied Housing Units with 3 People (U.S. Census) OWN4PERS10 2010 Owner-occupied Housing Units with 4 People (U.S. Census) OWN5PERS10 2010 Owner-occupied Housing Units with 5 People (U.S. Census) OWN6PERS10 2010 Owner-occupied Housing Units with 6 People (U.S. Census) OWN7PERS10 2010 Owner-occupied Housing Units with 7+ People (U.S. Census) OOSZBASE10 2010 Base for Owner-occupied Housing Units by Size (U.S. Census) RNT1PERS10 2010 Renter-occupied Housing Units with 1 Person (U.S. Census) RNT2PERS10 2010 Renter-occupied Housing Units with 2 People (U.S. Census) RNT3PERS10 2010 Renter-occupied Housing Units with 3 People (U.S. Census) RNT4PERS10 2010 Renter-occupied Housing Units with 4 People (U.S. Census) RNT5PERS10 2010 Renter-occupied Housing Units with 5 People (U.S. Census) RNT6PERS10 2010 Renter-occupied Housing Units with 6 People (U.S. Census) RNT7PERS10 2010 Renter-occupied Housing Units with 7+ People (U.S. Census) ROSZBASE10 2010 Base for Renter-occupied Housing Units by Size (U.S. Census) OOHHR15C10 2010 Owner-occupied Housing Units with a Householder Age 15-24 (U.S. Census) OOHHR25C10 2010 Owner-occupied Housing Units with a Householder Age 25-34 (U.S. Census) OOHHR35C10 2010 Owner-occupied Housing Units with a Householder Age 35-44 (U.S. Census) OOHHR45C10 2010 Owner-occupied Housing Units with a Householder Age 45-54 (U.S. Census) OOHHR55C10 2010 Owner-occupied Housing Units with a Householder Age 55-64 (U.S. Census) OOHHR65C10 2010 Owner-occupied Housing Units with a Householder Age 65-74 (U.S. Census) OOHHR75C10 2010 Owner-occupied Housing Units with a Householder Age 75-84 (U.S. Census) OOHHR85C10 2010 Owner-occupied Housing Units with a Householder Age 85+ (U.S. Census) ROHHR15C10 2010 Renter-occupied Housing Units with a Householder Age 15-24 (U.S. Census) ROHHR25C10 2010 Renter-occupied Housing Units with a Householder Age 25-34 (U.S. Census) ROHHR35C10 2010 Renter-occupied Housing Units with a Householder Age 35-44 (U.S. Census) ROHHR45C10 2010 Renter-occupied Housing Units with a Householder Age 45-54 (U.S. Census) ROHHR55C10 2010 Renter-occupied Housing Units with a Householder Age 55-64 (U.S. Census) ROHHR65C10 2010 Renter-occupied Housing Units with a Householder Age 65-74 (U.S. Census) ROHHR75C10 2010 Renter-occupied Housing Units with a Householder Age 75-84 (U.S. Census) ROHHR85C10 2010 Renter-occupied Housing Units with a Householder Age 85+ (U.S. Census) VACANT10 2010 Vacant Housing Units (U.S. Census) VACRENT10 2010 Vacant Housing Units: For Rent (U.S. Census) VACRNTED10 2010 Vacant Housing Units: Rented - Not Occupied (U.S. Census) VACSALE10 2010 Vacant Housing Units: For Sale Only (U.S. Census) VACSOLD10 2010 Vacant Housing Units: Sold - Not Occupied (U.S. Census) VACSEAS10 2010 Vacant Housing Units: Seasonal/Recreational/Occasional Use (U.S. Census) VACMIGR10 2010 Vacant Housing Units: For Migrant Workers (U.S. Census) VACOTHER10 2010 Vacant Housing Units: Other Vacant (U.S. Census) VACBASE10 2010 Base for Vacant Housing Units by Vacancy Status (U.S. Census) TOTPOP00 2000 Total Population (U.S. Census) HHPOP00 2000 Population in Households (U.S. Census) FAMPOP00 2000 Population in Families (U.S. Census) TOTHH00 2000 Total Households (U.S. Census) FAMHH00 2000 Total Family Households (U.S. Census) TOTHU00 2000 Total Housing Units (U.S. Census) OWNER00 2000 Owner Occupied Housing Units (U.S. Census) RENTER00 2000 Renter Occupied Housing Units (U.S. Census) TSEGNUM 2018 Dominant Tapestry Number (Esri) TSEGCODE 2018 Dominant Tapestry Segment (Esri) TSEGNAME 2018 Dominant Tapestry Segment Name (Esri) THHBASE 2018 Base for Tapestry Segmentation Households (Esri) THH01 2018 Top Tier (1A) Tapestry Households (Esri) THH02 2018 Professional Pride (1B) Tapestry Households (Esri) THH03 2018 Boomburbs (1C) Tapestry Households (Esri) THH04 2018 Savvy Suburbanites (1D) Tapestry Households (Esri) THH05 2018 Exurbanites (1E) Tapestry Households (Esri) THH06 2018 Urban Chic (2A) Tapestry Households (Esri) THH07 2018 Pleasantville (2B) Tapestry Households (Esri) THH08 2018 Pacific Heights (2C) Tapestry Households (Esri) THH09 2018 Enterprising Professionals (2D) Tapestry Households (Esri) THH10 2018 Laptops and Lattes (3A) Tapestry Households (Esri) THH11 2018 Metro Renters (3B) Tapestry Households (Esri) THH12 2018 Trendsetters (3C) Tapestry Households (Esri) THH13 2018 Soccer Moms (4A) Tapestry Households (Esri) THH14 2018 Home Improvement (4B) Tapestry Households (Esri) THH15 2018 Middleburg (4C) Tapestry Households (Esri) THH16 2018 Comfortable Empty Nesters (5A) Tapestry Households (Esri) THH17 2018 In Style (5B) Tapestry Households (Esri) THH18 2018 Parks and Rec (5C) Tapestry Households (Esri) THH19 2018 Rustbelt Traditions (5D) Tapestry Households (Esri) THH20 2018 Midlife Constants (5E) Tapestry Households (Esri) THH21 2018 Green Acres (6A) Tapestry Households (Esri) THH22 2018 Salt of the Earth (6B) Tapestry Households (Esri) THH23 2018 The Great Outdoors (6C) Tapestry Households (Esri) THH24 2018 Prairie Living (6D) Tapestry Households (Esri) THH25 2018 Rural Resort Dwellers (6E) Tapestry Households (Esri) THH26 2018 Heartland Communities (6F) Tapestry Households (Esri) THH27 2018 Up and Coming Families (7A) Tapestry Households (Esri) THH28 2018 Urban Villages (7B) Tapestry Households (Esri) THH29 2018 American Dreamers (7C) Tapestry Households (Esri) THH30 2018 Barrios Urbanos (7D) Tapestry Households (Esri) THH31 2018 Valley Growers (7E) Tapestry Households (Esri) THH32 2018 Southwestern Families (7F) Tapestry Households (Esri) THH33 2018 City Lights (8A) Tapestry Households (Esri) THH34 2018 Emerald City (8B) Tapestry Households (Esri) THH35 2018 Bright Young Professionals (8C) Tapestry Households (Esri) THH36 2018 Downtown Melting Pot (8D) Tapestry Households (Esri) THH37 2018 Front Porches (8E) Tapestry Households (Esri) THH38 2018 Old and Newcomers (8F) Tapestry Households (Esri) THH39 2018 Hardscrabble Road (8G) Tapestry Households (Esri) THH40 2018 Silver & Gold (9A) Tapestry Households (Esri) THH41 2018 Golden Years (9B) Tapestry Households (Esri) THH42 2018 The Elders (9C) Tapestry Households (Esri) THH43 2018 Senior Escapes (9D) Tapestry Households (Esri) THH44 2018 Retirement Communities (9E) Tapestry Households (Esri) THH45 2018 Social Security Set (9F) Tapestry Households (Esri) THH46 2018 Southern Satellites (10A) Tapestry Households (Esri) THH47 2018 Rooted Rural (10B) Tapestry Households (Esri) THH48 2018 Diners & Miners (10C) Tapestry Households (Esri) THH49 2018 Down the Road (10D) Tapestry Households (Esri) THH50 2018 Rural Bypasses (10E) Tapestry Households (Esri) THH51 2018 City Strivers (11A) Tapestry Households (Esri) THH52 2018 Young and Restless (11B) Tapestry Households (Esri) THH53 2018 Metro Fusion (11C) Tapestry Households (Esri) THH54 2018 Set to Impress (11D) Tapestry Households (Esri) THH55 2018 City Commons (11E) Tapestry Households (Esri) THH56 2018 Family Foundations (12A) Tapestry Households (Esri) THH57 2018 Traditional Living (12B) Tapestry Households (Esri) THH58 2018 Small Town Simplicity (12C) Tapestry Households (Esri) THH59 2018 Modest Income Homes (12D) Tapestry Households (Esri) THH60 2018 International Marketplace (13A) Tapestry Households (Esri) THH61 2018 Las Casas (13B) Tapestry Households (Esri) THH62 2018 NeWest Residents (13C) Tapestry Households (Esri) THH63 2018 Fresh Ambitions (13D) Tapestry Households (Esri) THH64 2018 High Rise Renters (13E) Tapestry Households (Esri) THH65 2018 Military Proximity (14A) Tapestry Households (Esri) THH66 2018 College Towns (14B) Tapestry Households (Esri) THH67 2018 Dorms to Diplomas (14C) Tapestry Households (Esri) THH68 2018 Unclassified (15) Tapestry Households (Esri) TLIFECODE 2018 Dominant Tapestry LifeMode Group Code TLIFENAME 2018 Dominant Tapestry LifeMode Group Name THHGRPL1 2018 Affluent Estates Tapestry LifeMode Group L1 Households (Esri) THHGRPL2 2018 Upscale Avenues Tapestry LifeMode Group L2 Households (Esri) THHGRPL3 2018 Uptown Individuals Tapestry LifeMode Group L3 Households (Esri) THHGRPL4 2018 Family Landscapes Tapestry LifeMode Group L4 Households (Esri) THHGRPL5 2018 GenXurban Tapestry LifeMode Group L5 Households (Esri) THHGRPL6 2018 Cozy Country Living Tapestry LifeMode Group L6 Households (Esri) THHGRPL7 2018 Ethnic Enclaves Tapestry LifeMode Group L7 Households (Esri) THHGRPL8 2018 Middle Ground Tapestry LifeMode Group L8 Households (Esri) THHGRPL9 2018 Senior Styles Tapestry LifeMode Group L9 Households (Esri) THHGRPL10 2018 Rustic Outposts Tapestry LifeMode Group L10 Households (Esri) THHGRPL11 2018 Midtown Singles Tapestry LifeMode Group L11 Households (Esri) THHGRPL12 2018 Hometown Tapestry LifeMode Group L12 Households (Esri) THHGRPL13 2018 Next Wave Tapestry LifeMode Group L13 Households (Esri) THHGRPL14 2018 Scholars and Patriots Tapestry LifeMode Group L14 Households (Esri) THHGRPU1 2018 Principal Urban Center Tapestry Urbanization Group U1 Households (Esri) THHGRPU2 2018 Urban Periphery Tapestry Urbanization Group U2 Households (Esri) THHGRPU3 2018 Metro Cities Tapestry Urbanization Group U3 Households (Esri) THHGRPU4 2018 Suburban Periphery Tapestry Urbanization Group U4 Households (Esri) THHGRPU5 2018 Semirural Tapestry Urbanization Group U5 Households (Esri) THHGRPU6 2018 Rural Tapestry Urbanization Group U6 Households (Esri) TADULTBASE 2018 Base for Tapestry Segmentation Adult Population (Esri) TADULT01 2018 Top Tier (1A) Tapestry Adult Population (Esri) TADULT02 2018 Professional Pride (1B) Tapestry Adult Population (Esri) TADULT03 2018 Boomburbs (1C) Tapestry Adult Population (Esri) TADULT04 2018 Savvy Suburbanites (1D) Tapestry Adult Population (Esri) TADULT05 2018 Exurbanites (1E) Tapestry Adult Population (Esri) TADULT06 2018 Urban Chic (2A) Tapestry Adult Population (Esri) TADULT07 2018 Pleasantville (2B) Tapestry Adult Population (Esri) TADULT08 2018 Pacific Heights (2C)Tapestry Adult Population (Esri) TADULT09 2018 Enterprising Professionals (2D) Tapestry Adult Population (Esri) TADULT10 2018 Laptops and Lattes (3A) Tapestry Adult Population (Esri) TADULT11 2018 Metro Renters (3B) Tapestry Adult Population (Esri) TADULT12 2018 Trendsetters (3C) Tapestry Adult Population (Esri) TADULT13 2018 Soccer Moms (4A) Tapestry Adult Population (Esri) TADULT14 2018 Home Improvement (4B) Tapestry Adult Population (Esri) TADULT15 2018 Middleburg (4C) Tapestry Adult Population (Esri) TADULT16 2018 Comfortable Empty Nesters (5A) Tapestry Adult Population (Esri) TADULT17 2018 In Style (5B) Tapestry Adult Population (Esri) TADULT18 2018 Parks and Rec (5C) Tapestry Adult Population (Esri) TADULT19 2018 Rustbelt Traditions (5D) Tapestry Adult Population (Esri) TADULT20 2018 Midlife Constants (5E) Tapestry Adult Population (Esri) TADULT21 2018 Green Acres (6A) Tapestry Adult Population (Esri) TADULT22 2018 Salt of the Earth (6B) Tapestry Adult Population (Esri) TADULT23 2018 The Great Outdoors (6C) Tapestry Adult Population (Esri) TADULT24 2018 Prairie Living (6D) Tapestry Adult Population (Esri) TADULT25 2018 Rural Resort Dwellers (6E) Tapestry Adult Population (Esri) TADULT26 2018 Heartland Communities (6F) Tapestry Adult Population (Esri) TADULT27 2018 Up and Coming Families (7A) Tapestry Adult Population (Esri) TADULT28 2018 Urban Villages (7B) Tapestry Adult Population (Esri) TADULT29 2018 American Dreamers (7C) Tapestry Adult Population (Esri) TADULT30 2018 Barrios Urbanos (7D) Tapestry Adult Population (Esri) TADULT31 2018 Valley Growers (7E) Tapestry Adult Population (Esri) TADULT32 2018 Southwestern Families (7F) Tapestry Adult Population (Esri) TADULT33 2018 City Lights (8A) Tapestry Adult Population (Esri) TADULT34 2018 Emerald City (8B) Tapestry Adult Population (Esri) TADULT35 2018 Bright Young Professionals (8C) Tapestry Adult Population (Esri) TADULT36 2018 Downtown Melting Pot (8D) Tapestry Adult Population (Esri) TADULT37 2018 Front Porches (8E) Tapestry Adult Population (Esri) TADULT38 2018 Old and Newcomers (8F) Tapestry Adult Population (Esri) TADULT39 2018 Hardscrabble Road (8G) Tapestry Adult Population (Esri) TADULT40 2018 Silver & Gold (9A) Tapestry Adult Population (Esri) TADULT41 2018 Golden Years (9B) Tapestry Adult Population (Esri) TADULT42 2018 The Elders (9C) Tapestry Adult Population (Esri) TADULT43 2018 Senior Escapes (9D) Tapestry Adult Population (Esri) TADULT44 2018 Retirement Communities (9E) Tapestry Adult Population (Esri) TADULT45 2018 Social Security Set (9F) Tapestry Adult Population (Esri) TADULT46 2018 Southern Satellites (10A) Tapestry Adult Population (Esri) TADULT47 2018 Rooted Rural (10B) Tapestry Adult Population (Esri) TADULT48 2018 Diners & Miners (10C) Tapestry Adult Population (Esri) TADULT49 2018 Down the Road (10D) Tapestry Adult Population (Esri) TADULT50 2018 Rural Bypasses (10E) Tapestry Adult Population (Esri) TADULT51 2018 City Strivers (11A) Tapestry Adult Population (Esri) TADULT52 2018 Young and Restless (11B) Tapestry Adult Population (Esri) TADULT53 2018 Metro Fusion (11C) Tapestry Adult Population (Esri) TADULT54 2018 Set to Impress (11D) Tapestry Adult Population (Esri) TADULT55 2018 City Commons (11E) Tapestry Adult Population (Esri) TADULT56 2018 Family Foundations (12A) Tapestry Adult Population (Esri) TADULT57 2018 Traditional Living (12B) Tapestry Adult Population (Esri) TADULT58 2018 Small Town Simplicity (12C) Tapestry Adult Population (Esri) TADULT59 2018 Modest Income Homes (12D) Tapestry Adult Population (Esri) TADULT60 2018 International Marketplace (13A) Tapestry Adult Population (Esri) TADULT61 2018 Las Casas (13B) Tapestry Adult Population (Esri) TADULT62 2018 NeWest Residents (13C) Tapestry Adult Population (Esri) TADULT63 2018 Fresh Ambitions (13D) Tapestry Adult Population (Esri) TADULT64 2018 High Rise Renters (13E) Tapestry Adult Population (Esri) TADULT65 2018 Military Proximity (14A) Tapestry Adult Population (Esri) TADULT66 2018 College Towns (14B) Tapestry Adult Population (Esri) TADULT67 2018 Dorms to Diplomas (14C) Tapestry Adult Population (Esri) TADULT68 2018 Unclassified (15) Tapestry Adult Population (Esri) Shape_Length Shape_Length Shape_Area Shape_Area
# Removing columns for 2023,2010,2000
state_df = state_df.loc[:,~state_df.columns.str.contains('FY|10|00')]
len(state_df.columns)
894
# Removing columns for individual age
state_df = state_df.loc[:,~state_df.columns.str.startswith(('AGE','MAGE','FAGE'))]
len(state_df.columns)
640
# Removing columns for Industry, Occupation
state_df = state_df.loc[:,~state_df.columns.str.startswith(('IND','OCC'))]
len(state_df.columns)
596
# Removing Individual Income Columns
state_df = state_df.loc[:,~state_df.columns.str.contains('A15|A25|A35|A45|A55|A65|A75')]
len(state_df.columns)
366
# Removing columns for Disposable Income and Net Worth
state_df = state_df.loc[:,~state_df.columns.str.startswith(('DI','NW'))]
len(state_df.columns)
348
# Removing columns for Tapestry Segmentation
state_df = state_df.loc[:,~state_df.columns.str.startswith(('TSE','THH','TADULT'))]
len(state_df.columns)
# Removing columns for Home Value
state_df = state_df.loc[:,~state_df.columns.str.startswith(('VAL'))]
len(state_df.columns)
state_df.columns
Index(['AAGEBASECY', 'AGGDI_CY', 'AGGHINC_CY', 'AGGINC_CY', 'AGGNW_CY',
'AIFBASE_CY', 'AIMBASE_CY', 'AMERIND_CY', 'AREA', 'ASIAN_CY',
...
'VAL250K_CY', 'VAL2M_CY', 'VAL50K_CY', 'VAL750K_CY', 'VALBASE_CY',
'WAGEBASECY', 'WHITE_CY', 'WHTFBASECY', 'WHTMBASECY', 'WIDOWED_CY'],
dtype='object', length=348)
print(obgyn_count_df.shape)
print(state_df.shape)
(56, 2) (51, 348)
# Merge provider count and women_df at state level
newstate_obgyn_df = pd.merge(obgyn_count_df,state_df,left_on='RegionAbbr', right_on='ST_ABBREV',how='inner')
newstate_obgyn_df.shape
(51, 350)
test_newstate_df = newstate_obgyn_df.copy()
test_newstate_df.head()
| RegionAbbr | Provider_Count | AAGEBASECY | AGGDI_CY | AGGHINC_CY | AGGINC_CY | AGGNW_CY | AIFBASE_CY | AIMBASE_CY | AMERIND_CY | AREA | ASIAN_CY | ASNFBASECY | ASNMBASECY | ASSCDEG_CY | AVGDI_CY | AVGFMSZ_CY | AVGHHSZ_CY | AVGHINC_CY | AVGNW_CY | AVGVAL_CY | BABYBOOMCY | BACHDEG_CY | BAGEBASECY | BLACK_CY | BLKFBASECY | BLKMBASECY | CIVLBFR_CY | EDUCBASECY | EMP_CY | FAMHH_CY | FAMPOP_CY | FEM0_CY | FEM15_CY | FEM18UP_CY | FEM20_CY | FEM21UP_CY | FEM25_CY | FEM30_CY | FEM35_CY | FEM40_CY | FEM45_CY | FEM50_CY | FEM55_CY | FEM5_CY | FEM60_CY | FEM65_CY | FEM70_CY | FEM75_CY | FEM80_CY | FEM85_CY | FEMALES_CY | GED_CY | GENALPHACY | GENBASE_CY | GENX_CY | GENZ_CY | GQPOP_CY | GRADDEG_CY | HAGEBASECY | HHPOP_CY | HINC0_CY | HINC150_CY | HINC15_CY | HINC25_CY | HINC35_CY | HINC50_CY | HINC75_CY | HINCBASECY | HISPAI_CY | HISPASN_CY | HISPBLK_CY | HISPMLT_CY | HISPOTH_CY | HISPPI_CY | HISPPOP_CY | HISPWHT_CY | HSGRAD_CY | HSPFBASECY | HSPMBASECY | IAGEBASECY | ID | LANDAREA | MAL18UP_CY | MAL21UP_CY | MALE0_CY | MALE15_CY | MALE20_CY | MALE25_CY | MALE30_CY | MALE35_CY | MALE40_CY | MALE45_CY | MALE50_CY | MALE55_CY | MALE5_CY | MALE60_CY | MALE65_CY | MALE70_CY | MALE75_CY | MALE80_CY | MALE85_CY | MALES_CY | MARBASE_CY | MARRIED_CY | MEDAGE_CY | MEDDI_CY | MEDFAGE_CY | MEDHHR_CY | MEDHINC_CY | MEDMAGE_CY | MEDNW_CY | MEDVAL_CY | MILLENN_CY | MINORITYCY | MLTFBASECY | MLTMBASECY | NAME | NEVMARR_CY | NHSPAI_CY | NHSPASN_CY | NHSPBLK_CY | NHSPMLT_CY | NHSPOTH_CY | NHSPPI_CY | NHSPWHT_CY | NOHS_CY | NONHISP_CY | OAGEBASECY | OBJECTID | OLDRGENSCY | OTHFBASECY | OTHMBASECY | OTHRACE_CY | OWNER_CY | PACIFIC_CY | PAGEBASECY | PCI_CY | PIFBASE_CY | PIMBASE_CY | POP0_CY | POP15_CY | POP18UP_CY | POP20_CY | POP21UP_CY | POP25_CY | POP30_CY | POP35_CY | POP40_CY | POP45_CY | POP50_CY | POP55_CY | POP5_CY | POP60_CY | POP65_CY | POP70_CY | POP75_CY | POP80_CY | POP85_CY | POPDENS_CY | RACE2UP_CY | RACEBASECY | RENTER_CY | SHAPE | SMCOLL_CY | SOMEHS_CY | STATE_NAME | ST_ABBREV | Shape_Area | Shape_Length | TADULT01 | TADULT02 | TADULT03 | TADULT04 | TADULT05 | TADULT06 | TADULT07 | TADULT08 | TADULT09 | TADULT11 | TADULT12 | TADULT13 | TADULT14 | TADULT15 | TADULT16 | TADULT17 | TADULT18 | TADULT19 | TADULT20 | TADULT21 | TADULT22 | TADULT23 | TADULT24 | TADULT25 | TADULT26 | TADULT27 | TADULT28 | TADULT29 | TADULT30 | TADULT31 | TADULT32 | TADULT33 | TADULT34 | TADULT35 | TADULT36 | TADULT37 | TADULT38 | TADULT39 | TADULT40 | TADULT41 | TADULT42 | TADULT43 | TADULT44 | TADULT45 | TADULT46 | TADULT47 | TADULT48 | TADULT49 | TADULT50 | TADULT51 | TADULT52 | TADULT53 | TADULT54 | TADULT55 | TADULT56 | TADULT57 | TADULT58 | TADULT59 | TADULT60 | TADULT61 | TADULT62 | TADULT63 | TADULT64 | TADULT65 | TADULT66 | TADULT67 | TADULT68 | TADULTBASE | THH01 | THH02 | THH03 | THH04 | THH05 | THH06 | THH07 | THH08 | THH09 | THH11 | THH12 | THH13 | THH14 | THH15 | THH16 | THH17 | THH18 | THH19 | THH20 | THH21 | THH22 | THH23 | THH24 | THH25 | THH26 | THH27 | THH28 | THH29 | THH30 | THH31 | THH32 | THH33 | THH34 | THH35 | THH36 | THH37 | THH38 | THH39 | THH40 | THH41 | THH42 | THH43 | THH44 | THH45 | THH46 | THH47 | THH48 | THH49 | THH50 | THH51 | THH52 | THH53 | THH54 | THH55 | THH56 | THH57 | THH58 | THH59 | THH60 | THH61 | THH62 | THH63 | THH64 | THH65 | THH66 | THH67 | THH68 | THHBASE | THHGRPL1 | THHGRPL11 | THHGRPL12 | THHGRPL13 | THHGRPL14 | THHGRPL2 | THHGRPL3 | THHGRPL4 | THHGRPL5 | THHGRPL6 | THHGRPL7 | THHGRPL8 | THHGRPL9 | THHGRPU1 | THHGRPU2 | THHGRPU3 | THHGRPU4 | THHGRPU5 | THHGRPU6 | TLIFECODE | TLIFENAME | TOTHH_CY | TOTHU_CY | TOTPOP_CY | TSEGCODE | TSEGNAME | TSEGNUM | UNEMPRT_CY | UNEMP_CY | VACANT_CY | VAL0_CY | VAL150K_CY | VAL1M_CY | VAL1PT5MCY | VAL250K_CY | VAL2M_CY | VAL50K_CY | VAL750K_CY | VALBASE_CY | WAGEBASECY | WHITE_CY | WHTFBASECY | WHTMBASECY | WIDOWED_CY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CA | 7353 | 5808539 | 984770070408 | 1341852609490 | 1363545956974 | 10953288740347 | 186300 | 188412 | 374712 | 158042.920912 | 5808539 | 3057816 | 2750723 | 2056384 | 73844 | 3.50 | 2.92 | 100620 | 821339 | 637422 | 8055682 | 5552919 | 2345048 | 2345048 | 1186331 | 1158717 | 19595308 | 26629106 | 18678853 | 9166028 | 32063051 | 1246195 | 1267704 | 15535129 | 1399534 | 14709444 | 1533458 | 1442776 | 1332317 | 1220853 | 1252603 | 1262625 | 1290328 | 1247274 | 1173091 | 1008278 | 746364 | 527232 | 362402 | 447803 | 20024285 | 621048 | 1008060 | 39806791 | 8011720 | 9289070 | 813593 | 3342632 | 15757962 | 38993198 | 1280615 | 1084396 | 1102410 | 1036596 | 1486913 | 2149782 | 1630222 | 13335897 | 214901 | 94837 | 149373 | 953706 | 7017528 | 17243 | 15757962 | 7310374 | 4804568 | 7811593 | 7946369 | 374712 | 06 | 155779.2198 | 15076422 | 14203971 | 1298461 | 1348980 | 1481021 | 1618901 | 1503701 | 1378564 | 1227529 | 1251002 | 1245261 | 1228175 | 1301504 | 1076510 | 891235 | 637267 | 427533 | 272897 | 270401 | 19782506 | 32126345 | 15627854 | 36.2 | 56020 | 37.3 | 51.5 | 69051 | 35.1 | 99297 | 505800 | 10636136 | 25228888 | 1076167 | 1053726 | California | 11926897 | 159811 | 5713702 | 2195675 | 1176187 | 84542 | 141009 | 14577903 | 2557052 | 24048829 | 7102070 | 5 | 2806123 | 3484353 | 3617717 | 7102070 | 7294468 | 158252 | 158252 | 34254 | 79878 | 78374 | 2544656 | 2616684 | 30611551 | 2880555 | 28913415 | 3152359 | 2946477 | 2710881 | 2448382 | 2503605 | 2507886 | 2518503 | 2548778 | 2249601 | 1899513 | 1383631 | 954765 | 635299 | 718204 | 255.5 | 2129893 | 39806791 | 6041636 | {'rings': [[[-13038833.5744, 3845968.519900001... | 5627462 | 2067041 | California | CA | 6.458207e+11 | 5.955195e+06 | 962752 | 447008 | 877339 | 707623 | 1030736 | 998289 | 1614673 | 1324877 | 854053 | 428180 | 1051926 | 640890 | 507501 | 147970 | 237918 | 290669 | 232487 | 111099 | 175840 | 175248 | 27484 | 508845 | 34216 | 94745 | 26920 | 657657 | 2654588 | 898660 | 530885 | 587455 | 321133 | 1177243 | 208733 | 337293 | 394628 | 476335 | 299210 | 59480 | 140493 | 366232 | 156521 | 289040 | 189701 | 202710 | 74475 | 54655 | 10628 | 210357 | 24920 | 79246 | 244837 | 409205 | 166215 | 21790 | 118065 | 25748 | 113377 | 9029 | 1432303 | 1875298 | 534518 | 504305 | 79548 | 103401 | 172871 | 214308 | 121327 | 30611551 | 435771 | 183824 | 367891 | 297569 | 476909 | 507339 | 673663 | 508597 | 428604 | 259020 | 577307 | 263847 | 213335 | 66627 | 109074 | 143993 | 108873 | 52243 | 85167 | 78061 | 12137 | 244469 | 15552 | 49244 | 13249 | 260783 | 911215 | 362738 | 195394 | 210403 | 128040 | 543776 | 111747 | 169541 | 165386 | 226491 | 161769 | 28909 | 76061 | 193740 | 98928 | 146079 | 107181 | 112822 | 31553 | 22139 | 5084 | 95731 | 9515 | 37825 | 135236 | 200618 | 89406 | 11253 | 50951 | 12199 | 59308 | 4548 | 605661 | 641139 | 209939 | 214632 | 38327 | 25756 | 76824 | 50008 | 179 | 13336104 | 1761964 | 474338 | 127006 | 1709698 | 152588 | 2118203 | 1161232 | 543809 | 499350 | 412712 | 2068573 | 1407619 | 734811 | 1827341 | 4374461 | 1267838 | 4807134 | 591397 | 467754 | 2 | Upscale Avenues | 13336104 | 14383561 | 39806791 | 7B | Urban Villages | 28 | 4.7 | 916455 | 1047457 | 190476 | 277000 | 678744 | 195039 | 432916 | 242837 | 160723 | 903689 | 7293065 | 21888277 | 21888277 | 10953440 | 10934837 | 1601462 |
| 1 | TX | 5115 | 1428081 | 663797428676 | 846781778790 | 860166554925 | 6544101733836 | 99642 | 101050 | 200692 | 264622.431161 | 1428081 | 736438 | 691643 | 1352089 | 65007 | 3.36 | 2.78 | 82927 | 640876 | 234140 | 5610338 | 3595450 | 3554094 | 3554094 | 1837649 | 1716445 | 13994294 | 18710254 | 13323060 | 7102474 | 23868426 | 1012163 | 958641 | 11007087 | 1023650 | 10392959 | 1088542 | 1028122 | 990111 | 912087 | 908014 | 888197 | 906409 | 1016393 | 815633 | 699159 | 508500 | 349890 | 230272 | 255883 | 14596063 | 796406 | 820030 | 28954616 | 5795902 | 7332348 | 603396 | 1902087 | 11500677 | 28351220 | 1099136 | 601595 | 972153 | 998707 | 1339610 | 1837748 | 1220161 | 10211181 | 108000 | 21467 | 126710 | 446880 | 3229534 | 4993 | 11500677 | 7563093 | 3900291 | 5712914 | 5787763 | 200692 | 48 | 261231.7115 | 10619025 | 9978508 | 1049926 | 1010959 | 1068334 | 1146033 | 1057803 | 1000356 | 900336 | 893942 | 864949 | 855845 | 1054854 | 749300 | 623922 | 440199 | 284583 | 169339 | 142828 | 14358553 | 22771838 | 11608584 | 34.8 | 48504 | 35.8 | 49.5 | 57286 | 33.8 | 89454 | 173734 | 7624216 | 16954978 | 459123 | 454026 | Texas | 7616564 | 92692 | 1406614 | 3427384 | 466269 | 37515 | 23827 | 11999638 | 1572787 | 17453939 | 3267049 | 44 | 1771782 | 1571022 | 1696027 | 3267049 | 6286745 | 28820 | 28820 | 29707 | 14439 | 14381 | 2062089 | 1969600 | 21626112 | 2091984 | 20371467 | 2234575 | 2085925 | 1990467 | 1812423 | 1801956 | 1753146 | 1762254 | 2071247 | 1564933 | 1323081 | 948699 | 634473 | 399611 | 398711 | 110.8 | 913149 | 28954616 | 3924542 | {'rings': [[[-10822386.4159, 2999262.775600001... | 4037002 | 1554142 | Texas | TX | 9.460813e+11 | 9.621068e+06 | 257841 | 546026 | 1102103 | 504579 | 298473 | 141680 | 25966 | 3941 | 232406 | 435495 | 27305 | 751696 | 718565 | 539687 | 401342 | 297639 | 111651 | 342816 | 282798 | 522498 | 225754 | 193715 | 190129 | 160088 | 240532 | 1662778 | 199271 | 827432 | 1492651 | 7383 | 1369349 | 27222 | 182557 | 574840 | 1656 | 145357 | 286509 | 195161 | 82795 | 95111 | 17082 | 114932 | 97451 | 70012 | 854973 | 411283 | 295295 | 344420 | 112278 | 4120 | 700747 | 410891 | 133118 | 94734 | 161445 | 243184 | 227922 | 183344 | 38365 | 84691 | 621868 | 44070 | 24312 | 72359 | 201481 | 199469 | 60997 | 21626112 | 124524 | 236631 | 510390 | 229203 | 148579 | 78268 | 11804 | 1715 | 123382 | 270425 | 15874 | 334179 | 322524 | 256069 | 194257 | 156003 | 54214 | 166876 | 141026 | 242077 | 106842 | 94754 | 92548 | 81196 | 118859 | 746527 | 76855 | 359005 | 603599 | 3263 | 582375 | 10984 | 106059 | 311328 | 831 | 73537 | 158365 | 91562 | 43855 | 55012 | 10078 | 56954 | 56138 | 36794 | 392512 | 198958 | 135159 | 153282 | 47834 | 2198 | 438819 | 218771 | 73760 | 51764 | 73393 | 116283 | 113907 | 88717 | 18279 | 32041 | 289865 | 21319 | 12423 | 18523 | 95654 | 64280 | 157 | 10211287 | 1249327 | 785312 | 392300 | 373927 | 178457 | 215169 | 344348 | 912772 | 712376 | 736276 | 2371624 | 752666 | 258831 | 670984 | 2543938 | 1519018 | 3382976 | 702334 | 1391880 | 7 | Ethnic Enclaves | 10211287 | 11236543 | 28954616 | 7A | Up and Coming Families | 27 | 4.8 | 671234 | 1025256 | 561071 | 985102 | 66551 | 15750 | 527935 | 20493 | 1095680 | 94609 | 6285595 | 19562731 | 19562731 | 9877750 | 9684981 | 1165365 |
| 2 | NY | 5005 | 1774868 | 494056143933 | 721252426439 | 733561461654 | 5780147040071 | 57206 | 55978 | 113184 | 48359.759399 | 1774868 | 920657 | 854211 | 1234468 | 65662 | 3.27 | 2.59 | 95857 | 768205 | 451725 | 4524627 | 2844992 | 3202872 | 3202872 | 1720637 | 1482235 | 10297331 | 13980509 | 9750809 | 4711535 | 15422531 | 541010 | 622797 | 8316966 | 692667 | 7896904 | 736087 | 711718 | 665428 | 617325 | 653985 | 691792 | 725143 | 555213 | 671105 | 583941 | 447188 | 321376 | 227864 | 297383 | 10347030 | 556396 | 439969 | 20070143 | 4071725 | 4262278 | 575494 | 2207562 | 3920105 | 19494649 | 910026 | 562411 | 659740 | 628211 | 822195 | 1168671 | 908502 | 7524226 | 58228 | 16280 | 331729 | 299313 | 1562083 | 3773 | 3920105 | 1648699 | 3075758 | 1994498 | 1925607 | 113184 | 36 | 47126.3986 | 7603630 | 7176760 | 563187 | 645491 | 694720 | 735814 | 699391 | 649639 | 591431 | 624744 | 651555 | 664052 | 578913 | 595536 | 496269 | 361798 | 245015 | 158479 | 156451 | 9723113 | 16636184 | 7732110 | 39.0 | 51058 | 40.5 | 53.2 | 63751 | 37.6 | 87167 | 322649 | 5071787 | 9104863 | 369440 | 340441 | New York | 6469341 | 54956 | 1758588 | 2871143 | 410568 | 83316 | 6187 | 10965280 | 866788 | 16150038 | 1645399 | 33 | 1699757 | 825459 | 819940 | 1645399 | 3928983 | 9960 | 9960 | 36550 | 5029 | 4931 | 1104197 | 1268288 | 15920596 | 1387387 | 15073664 | 1471901 | 1411109 | 1315067 | 1208756 | 1278729 | 1343347 | 1389195 | 1134126 | 1266641 | 1080210 | 808986 | 566391 | 386343 | 453834 | 425.9 | 709881 | 20070143 | 3595416 | {'rings': [[[-8259979.4246, 4949745.592100002]... | 2182364 | 1012181 | New York | NY | 2.323782e+11 | 5.026351e+06 | 512090 | 105026 | 4623 | 566085 | 222285 | 250800 | 1375859 | 372083 | 36331 | 199329 | 565803 | 94595 | 113012 | 39543 | 373383 | 232188 | 170661 | 324706 | 344500 | 271067 | 329966 | 269307 | 19946 | 113275 | 239738 | 665 | 189396 | 20342 | 1016 | 1005 | 2888 | 696353 | 107184 | 38266 | 1333128 | 73019 | 173289 | 194976 | 25993 | 345965 | 23485 | 24115 | 145463 | 144351 | 204688 | 104636 | 1156 | 28446 | 11960 | 1150953 | 23619 | 17471 | 149676 | 107735 | 83723 | 278978 | 126444 | 50178 | 647696 | 52872 | 63074 | 73315 | 1056586 | 17171 | 106449 | 111054 | 39680 | 15920596 | 227930 | 45156 | 1971 | 245731 | 101807 | 129891 | 578041 | 153655 | 19294 | 114087 | 297684 | 42464 | 49670 | 19702 | 178348 | 115820 | 85493 | 169528 | 177703 | 127239 | 155409 | 132719 | 9562 | 57642 | 120941 | 284 | 63030 | 8326 | 352 | 535 | 1156 | 332041 | 60446 | 20774 | 569263 | 37915 | 95602 | 102941 | 13893 | 191240 | 11264 | 12914 | 83122 | 86221 | 98353 | 50421 | 584 | 14648 | 1916 | 521013 | 13514 | 9730 | 84095 | 58870 | 42581 | 150061 | 70282 | 28208 | 261620 | 17305 | 24745 | 34054 | 494486 | 4732 | 41867 | 10397 | 263 | 7524399 | 622595 | 687222 | 291132 | 832210 | 56996 | 880881 | 853619 | 111836 | 726892 | 603512 | 73683 | 1218982 | 398654 | 2497180 | 1108306 | 940871 | 2104912 | 239022 | 633845 | 8 | Middle Ground | 7524399 | 8391638 | 20070143 | 2B | Pleasantville | 7 | 5.3 | 546522 | 867239 | 152062 | 379625 | 179685 | 56246 | 246121 | 99717 | 374729 | 263656 | 3926227 | 12613979 | 12613979 | 6448602 | 6165377 | 977829 |
| 3 | FL | 4133 | 585936 | 487005072197 | 613723856820 | 624450149363 | 6548612367402 | 40665 | 42463 | 83128 | 56491.338012 | 585936 | 316402 | 269534 | 1468305 | 59737 | 3.03 | 2.51 | 75281 | 803267 | 282862 | 5125816 | 2763015 | 3423250 | 3423250 | 1775199 | 1648051 | 9939670 | 14984858 | 9428446 | 5273287 | 15994410 | 540535 | 575794 | 8672285 | 647413 | 8288558 | 693040 | 652863 | 622949 | 602161 | 654907 | 696088 | 756439 | 556332 | 740359 | 711079 | 574407 | 426317 | 297361 | 348670 | 10667264 | 630616 | 434241 | 20875686 | 4066365 | 4142104 | 433652 | 1591194 | 5399203 | 20442034 | 951439 | 366646 | 868205 | 873200 | 1196903 | 1521148 | 970483 | 8152474 | 30738 | 13113 | 201778 | 241103 | 848135 | 3477 | 5399203 | 4060859 | 3663685 | 2732325 | 2666878 | 83128 | 12 | 53624.7587 | 8132434 | 7737236 | 561572 | 604324 | 667070 | 728403 | 671610 | 629910 | 591323 | 640640 | 666976 | 693978 | 574960 | 651105 | 617390 | 497457 | 358273 | 236773 | 224380 | 10208422 | 17479459 | 8404539 | 42.3 | 43866 | 43.9 | 55.4 | 52098 | 40.6 | 98640 | 212954 | 4819710 | 9694483 | 320999 | 306853 | Florida | 5579880 | 52390 | 572823 | 3221472 | 386749 | 50104 | 11742 | 11181203 | 770061 | 15476483 | 898239 | 10 | 2287450 | 433750 | 464489 | 898239 | 5193134 | 15219 | 15219 | 29913 | 7485 | 7734 | 1102107 | 1180118 | 16804719 | 1314483 | 16025794 | 1421443 | 1324473 | 1252859 | 1193484 | 1295547 | 1363064 | 1450417 | 1131292 | 1391464 | 1328469 | 1071864 | 784590 | 534134 | 573050 | 389.3 | 627852 | 20875686 | 2959407 | {'rings': [[[-9124037.3451, 2817150.9723999985... | 2990780 | 1107202 | Florida | FL | 1.866243e+11 | 9.761724e+06 | 139564 | 203442 | 252298 | 342663 | 283344 | 129690 | 129485 | 3407 | 78849 | 131796 | 30196 | 479436 | 544452 | 543860 | 408053 | 288055 | 142999 | 417240 | 573770 | 261579 | 85398 | 333616 | 2029 | 115622 | 143470 | 540715 | 227391 | 933973 | 104898 | 70517 | 460679 | 56767 | 240719 | 452235 | 0 | 207742 | 413860 | 143688 | 644510 | 279740 | 779970 | 766863 | 377196 | 100898 | 428649 | 183680 | 8535 | 297341 | 166979 | 48101 | 323008 | 451913 | 233004 | 178840 | 285711 | 217009 | 234897 | 250408 | 90659 | 40210 | 127174 | 79439 | 13767 | 21497 | 103498 | 109161 | 20707 | 16804719 | 64080 | 84617 | 112750 | 150048 | 139641 | 68081 | 53419 | 1152 | 40455 | 79640 | 18059 | 206336 | 237762 | 248795 | 195362 | 149440 | 70108 | 204783 | 289645 | 118846 | 39620 | 162626 | 1011 | 60130 | 70532 | 238120 | 80416 | 393622 | 42245 | 27259 | 185836 | 27665 | 135609 | 234795 | 0 | 103443 | 231898 | 69296 | 347422 | 155361 | 465971 | 402057 | 222582 | 59214 | 190835 | 81384 | 3488 | 138256 | 62390 | 20779 | 187750 | 229070 | 131273 | 90284 | 117945 | 108972 | 124281 | 117164 | 41705 | 15882 | 54240 | 35494 | 7223 | 4930 | 49689 | 35423 | 57 | 8152541 | 551136 | 659156 | 468362 | 154544 | 90042 | 163107 | 111977 | 692893 | 909338 | 452765 | 967498 | 802706 | 1652607 | 229713 | 1692280 | 1574873 | 2924108 | 1011180 | 720330 | 9 | Senior Styles | 8152541 | 9790195 | 20875686 | 9C | The Elders | 42 | 5.1 | 511224 | 1637654 | 351168 | 819037 | 78889 | 20495 | 557404 | 36949 | 600047 | 111697 | 5192714 | 15242062 | 15242062 | 7772764 | 7469298 | 1199573 |
| 4 | PA | 3210 | 462360 | 315983343213 | 411902114464 | 418518490501 | 4125980999869 | 16036 | 16102 | 32138 | 45285.896438 | 462360 | 240313 | 222047 | 749637 | 61749 | 3.02 | 2.46 | 80493 | 806294 | 236846 | 3167219 | 1763503 | 1470953 | 1470953 | 765013 | 705940 | 6619516 | 9125523 | 6217293 | 3282447 | 9921694 | 335276 | 404682 | 5362136 | 433612 | 5076349 | 410643 | 397480 | 389122 | 375532 | 411895 | 447624 | 487329 | 355242 | 461925 | 408531 | 312098 | 231110 | 173218 | 235227 | 6642616 | 345422 | 268314 | 12992598 | 2573675 | 2724068 | 426311 | 1120165 | 987581 | 12566287 | 549498 | 299343 | 505620 | 485138 | 661013 | 933563 | 649666 | 5117219 | 13841 | 4066 | 72801 | 81708 | 393883 | 1223 | 987581 | 420059 | 2817515 | 484730 | 502851 | 32138 | 42 | 44742.7032 | 5010352 | 4726214 | 349790 | 417152 | 441081 | 428491 | 405440 | 392100 | 371081 | 405311 | 435283 | 463856 | 369830 | 429073 | 366389 | 265594 | 180573 | 120018 | 120580 | 6349982 | 10822050 | 5289824 | 41.4 | 46696 | 43.0 | 54.9 | 57362 | 39.8 | 126527 | 185452 | 2988934 | 3115252 | 161589 | 152711 | Pennsylvania | 3726095 | 18297 | 458294 | 1398152 | 232592 | 16338 | 3998 | 9877346 | 286064 | 12005017 | 410221 | 39 | 1270388 | 195431 | 214790 | 410221 | 3463374 | 5221 | 5221 | 32212 | 2622 | 2599 | 685066 | 821834 | 10372488 | 874693 | 9802563 | 839134 | 802920 | 781222 | 746613 | 817206 | 882907 | 951185 | 725072 | 890998 | 774920 | 577692 | 411683 | 293236 | 355807 | 290.4 | 314300 | 12992598 | 1653953 | {'rings': [[[-8382772.6493, 4844948.8923999965... | 1457813 | 585404 | Pennsylvania | PA | 2.053022e+11 | 2.068588e+06 | 162479 | 224406 | 22707 | 432943 | 210046 | 93639 | 202612 | 1754 | 84748 | 134652 | 24943 | 212966 | 62172 | 134424 | 548302 | 271213 | 517207 | 239410 | 553292 | 575890 | 903871 | 107575 | 38382 | 88283 | 504795 | 7672 | 14931 | 45556 | 6099 | 2557 | 6112 | 108325 | 136072 | 71952 | 14130 | 273695 | 196055 | 165969 | 5495 | 221381 | 17439 | 19227 | 190704 | 111879 | 144975 | 257849 | 36862 | 11142 | 18502 | 63122 | 23301 | 34327 | 146156 | 125056 | 113432 | 220217 | 317061 | 254390 | 17469 | 0 | 2886 | 271578 | 15924 | 0 | 143081 | 121146 | 29470 | 10372488 | 74277 | 98686 | 10729 | 196059 | 101319 | 45957 | 93730 | 645 | 45424 | 82738 | 12942 | 99476 | 28229 | 66028 | 267265 | 135979 | 258794 | 127103 | 278212 | 269579 | 435594 | 52290 | 18313 | 44342 | 263868 | 3585 | 6189 | 20132 | 2629 | 902 | 2884 | 53850 | 73109 | 39839 | 6765 | 137745 | 110110 | 84780 | 2896 | 116222 | 10022 | 10151 | 101801 | 66453 | 70968 | 124551 | 18223 | 5868 | 6096 | 31133 | 14285 | 17713 | 80722 | 66212 | 56799 | 116780 | 175679 | 129971 | 7816 | 0 | 1255 | 123001 | 7629 | 0 | 57457 | 25114 | 82 | 5117327 | 481070 | 210065 | 479229 | 139701 | 82571 | 185756 | 118011 | 193733 | 1067353 | 1083986 | 36321 | 506198 | 307545 | 287794 | 465570 | 1070547 | 1730882 | 522496 | 1039956 | 6 | Cozy Country Living | 5117327 | 5703807 | 12992598 | 6B | Salt of the Earth | 22 | 6.1 | 402223 | 586480 | 222750 | 599167 | 27840 | 4755 | 326829 | 6624 | 516090 | 45734 | 3463173 | 10297405 | 10297405 | 5261612 | 5035793 | 725053 |
test_newstate_df.drop(['RegionAbbr','OBJECTID','SHAPE','STATE_NAME','ST_ABBREV','NAME','Shape_Area','Shape_Length','TLIFENAME','TSEGCODE','TSEGNAME','ID','TLIFECODE'], axis=1, inplace=True)
test_newstate_df.head()
| Provider_Count | AAGEBASECY | AGGDI_CY | AGGHINC_CY | AGGINC_CY | AGGNW_CY | AIFBASE_CY | AIMBASE_CY | AMERIND_CY | AREA | ASIAN_CY | ASNFBASECY | ASNMBASECY | ASSCDEG_CY | AVGDI_CY | AVGFMSZ_CY | AVGHHSZ_CY | AVGHINC_CY | AVGNW_CY | AVGVAL_CY | BABYBOOMCY | BACHDEG_CY | BAGEBASECY | BLACK_CY | BLKFBASECY | BLKMBASECY | CIVLBFR_CY | EDUCBASECY | EMP_CY | FAMHH_CY | FAMPOP_CY | FEM0_CY | FEM15_CY | FEM18UP_CY | FEM20_CY | FEM21UP_CY | FEM25_CY | FEM30_CY | FEM35_CY | FEM40_CY | FEM45_CY | FEM50_CY | FEM55_CY | FEM5_CY | FEM60_CY | FEM65_CY | FEM70_CY | FEM75_CY | FEM80_CY | FEM85_CY | FEMALES_CY | GED_CY | GENALPHACY | GENBASE_CY | GENX_CY | GENZ_CY | GQPOP_CY | GRADDEG_CY | HAGEBASECY | HHPOP_CY | HINC0_CY | HINC150_CY | HINC15_CY | HINC25_CY | HINC35_CY | HINC50_CY | HINC75_CY | HINCBASECY | HISPAI_CY | HISPASN_CY | HISPBLK_CY | HISPMLT_CY | HISPOTH_CY | HISPPI_CY | HISPPOP_CY | HISPWHT_CY | HSGRAD_CY | HSPFBASECY | HSPMBASECY | IAGEBASECY | LANDAREA | MAL18UP_CY | MAL21UP_CY | MALE0_CY | MALE15_CY | MALE20_CY | MALE25_CY | MALE30_CY | MALE35_CY | MALE40_CY | MALE45_CY | MALE50_CY | MALE55_CY | MALE5_CY | MALE60_CY | MALE65_CY | MALE70_CY | MALE75_CY | MALE80_CY | MALE85_CY | MALES_CY | MARBASE_CY | MARRIED_CY | MEDAGE_CY | MEDDI_CY | MEDFAGE_CY | MEDHHR_CY | MEDHINC_CY | MEDMAGE_CY | MEDNW_CY | MEDVAL_CY | MILLENN_CY | MINORITYCY | MLTFBASECY | MLTMBASECY | NEVMARR_CY | NHSPAI_CY | NHSPASN_CY | NHSPBLK_CY | NHSPMLT_CY | NHSPOTH_CY | NHSPPI_CY | NHSPWHT_CY | NOHS_CY | NONHISP_CY | OAGEBASECY | OLDRGENSCY | OTHFBASECY | OTHMBASECY | OTHRACE_CY | OWNER_CY | PACIFIC_CY | PAGEBASECY | PCI_CY | PIFBASE_CY | PIMBASE_CY | POP0_CY | POP15_CY | POP18UP_CY | POP20_CY | POP21UP_CY | POP25_CY | POP30_CY | POP35_CY | POP40_CY | POP45_CY | POP50_CY | POP55_CY | POP5_CY | POP60_CY | POP65_CY | POP70_CY | POP75_CY | POP80_CY | POP85_CY | POPDENS_CY | RACE2UP_CY | RACEBASECY | RENTER_CY | SMCOLL_CY | SOMEHS_CY | TADULT01 | TADULT02 | TADULT03 | TADULT04 | TADULT05 | TADULT06 | TADULT07 | TADULT08 | TADULT09 | TADULT11 | TADULT12 | TADULT13 | TADULT14 | TADULT15 | TADULT16 | TADULT17 | TADULT18 | TADULT19 | TADULT20 | TADULT21 | TADULT22 | TADULT23 | TADULT24 | TADULT25 | TADULT26 | TADULT27 | TADULT28 | TADULT29 | TADULT30 | TADULT31 | TADULT32 | TADULT33 | TADULT34 | TADULT35 | TADULT36 | TADULT37 | TADULT38 | TADULT39 | TADULT40 | TADULT41 | TADULT42 | TADULT43 | TADULT44 | TADULT45 | TADULT46 | TADULT47 | TADULT48 | TADULT49 | TADULT50 | TADULT51 | TADULT52 | TADULT53 | TADULT54 | TADULT55 | TADULT56 | TADULT57 | TADULT58 | TADULT59 | TADULT60 | TADULT61 | TADULT62 | TADULT63 | TADULT64 | TADULT65 | TADULT66 | TADULT67 | TADULT68 | TADULTBASE | THH01 | THH02 | THH03 | THH04 | THH05 | THH06 | THH07 | THH08 | THH09 | THH11 | THH12 | THH13 | THH14 | THH15 | THH16 | THH17 | THH18 | THH19 | THH20 | THH21 | THH22 | THH23 | THH24 | THH25 | THH26 | THH27 | THH28 | THH29 | THH30 | THH31 | THH32 | THH33 | THH34 | THH35 | THH36 | THH37 | THH38 | THH39 | THH40 | THH41 | THH42 | THH43 | THH44 | THH45 | THH46 | THH47 | THH48 | THH49 | THH50 | THH51 | THH52 | THH53 | THH54 | THH55 | THH56 | THH57 | THH58 | THH59 | THH60 | THH61 | THH62 | THH63 | THH64 | THH65 | THH66 | THH67 | THH68 | THHBASE | THHGRPL1 | THHGRPL11 | THHGRPL12 | THHGRPL13 | THHGRPL14 | THHGRPL2 | THHGRPL3 | THHGRPL4 | THHGRPL5 | THHGRPL6 | THHGRPL7 | THHGRPL8 | THHGRPL9 | THHGRPU1 | THHGRPU2 | THHGRPU3 | THHGRPU4 | THHGRPU5 | THHGRPU6 | TOTHH_CY | TOTHU_CY | TOTPOP_CY | TSEGNUM | UNEMPRT_CY | UNEMP_CY | VACANT_CY | VAL0_CY | VAL150K_CY | VAL1M_CY | VAL1PT5MCY | VAL250K_CY | VAL2M_CY | VAL50K_CY | VAL750K_CY | VALBASE_CY | WAGEBASECY | WHITE_CY | WHTFBASECY | WHTMBASECY | WIDOWED_CY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7353 | 5808539 | 984770070408 | 1341852609490 | 1363545956974 | 10953288740347 | 186300 | 188412 | 374712 | 158042.920912 | 5808539 | 3057816 | 2750723 | 2056384 | 73844 | 3.50 | 2.92 | 100620 | 821339 | 637422 | 8055682 | 5552919 | 2345048 | 2345048 | 1186331 | 1158717 | 19595308 | 26629106 | 18678853 | 9166028 | 32063051 | 1246195 | 1267704 | 15535129 | 1399534 | 14709444 | 1533458 | 1442776 | 1332317 | 1220853 | 1252603 | 1262625 | 1290328 | 1247274 | 1173091 | 1008278 | 746364 | 527232 | 362402 | 447803 | 20024285 | 621048 | 1008060 | 39806791 | 8011720 | 9289070 | 813593 | 3342632 | 15757962 | 38993198 | 1280615 | 1084396 | 1102410 | 1036596 | 1486913 | 2149782 | 1630222 | 13335897 | 214901 | 94837 | 149373 | 953706 | 7017528 | 17243 | 15757962 | 7310374 | 4804568 | 7811593 | 7946369 | 374712 | 155779.2198 | 15076422 | 14203971 | 1298461 | 1348980 | 1481021 | 1618901 | 1503701 | 1378564 | 1227529 | 1251002 | 1245261 | 1228175 | 1301504 | 1076510 | 891235 | 637267 | 427533 | 272897 | 270401 | 19782506 | 32126345 | 15627854 | 36.2 | 56020 | 37.3 | 51.5 | 69051 | 35.1 | 99297 | 505800 | 10636136 | 25228888 | 1076167 | 1053726 | 11926897 | 159811 | 5713702 | 2195675 | 1176187 | 84542 | 141009 | 14577903 | 2557052 | 24048829 | 7102070 | 2806123 | 3484353 | 3617717 | 7102070 | 7294468 | 158252 | 158252 | 34254 | 79878 | 78374 | 2544656 | 2616684 | 30611551 | 2880555 | 28913415 | 3152359 | 2946477 | 2710881 | 2448382 | 2503605 | 2507886 | 2518503 | 2548778 | 2249601 | 1899513 | 1383631 | 954765 | 635299 | 718204 | 255.5 | 2129893 | 39806791 | 6041636 | 5627462 | 2067041 | 962752 | 447008 | 877339 | 707623 | 1030736 | 998289 | 1614673 | 1324877 | 854053 | 428180 | 1051926 | 640890 | 507501 | 147970 | 237918 | 290669 | 232487 | 111099 | 175840 | 175248 | 27484 | 508845 | 34216 | 94745 | 26920 | 657657 | 2654588 | 898660 | 530885 | 587455 | 321133 | 1177243 | 208733 | 337293 | 394628 | 476335 | 299210 | 59480 | 140493 | 366232 | 156521 | 289040 | 189701 | 202710 | 74475 | 54655 | 10628 | 210357 | 24920 | 79246 | 244837 | 409205 | 166215 | 21790 | 118065 | 25748 | 113377 | 9029 | 1432303 | 1875298 | 534518 | 504305 | 79548 | 103401 | 172871 | 214308 | 121327 | 30611551 | 435771 | 183824 | 367891 | 297569 | 476909 | 507339 | 673663 | 508597 | 428604 | 259020 | 577307 | 263847 | 213335 | 66627 | 109074 | 143993 | 108873 | 52243 | 85167 | 78061 | 12137 | 244469 | 15552 | 49244 | 13249 | 260783 | 911215 | 362738 | 195394 | 210403 | 128040 | 543776 | 111747 | 169541 | 165386 | 226491 | 161769 | 28909 | 76061 | 193740 | 98928 | 146079 | 107181 | 112822 | 31553 | 22139 | 5084 | 95731 | 9515 | 37825 | 135236 | 200618 | 89406 | 11253 | 50951 | 12199 | 59308 | 4548 | 605661 | 641139 | 209939 | 214632 | 38327 | 25756 | 76824 | 50008 | 179 | 13336104 | 1761964 | 474338 | 127006 | 1709698 | 152588 | 2118203 | 1161232 | 543809 | 499350 | 412712 | 2068573 | 1407619 | 734811 | 1827341 | 4374461 | 1267838 | 4807134 | 591397 | 467754 | 13336104 | 14383561 | 39806791 | 28 | 4.7 | 916455 | 1047457 | 190476 | 277000 | 678744 | 195039 | 432916 | 242837 | 160723 | 903689 | 7293065 | 21888277 | 21888277 | 10953440 | 10934837 | 1601462 |
| 1 | 5115 | 1428081 | 663797428676 | 846781778790 | 860166554925 | 6544101733836 | 99642 | 101050 | 200692 | 264622.431161 | 1428081 | 736438 | 691643 | 1352089 | 65007 | 3.36 | 2.78 | 82927 | 640876 | 234140 | 5610338 | 3595450 | 3554094 | 3554094 | 1837649 | 1716445 | 13994294 | 18710254 | 13323060 | 7102474 | 23868426 | 1012163 | 958641 | 11007087 | 1023650 | 10392959 | 1088542 | 1028122 | 990111 | 912087 | 908014 | 888197 | 906409 | 1016393 | 815633 | 699159 | 508500 | 349890 | 230272 | 255883 | 14596063 | 796406 | 820030 | 28954616 | 5795902 | 7332348 | 603396 | 1902087 | 11500677 | 28351220 | 1099136 | 601595 | 972153 | 998707 | 1339610 | 1837748 | 1220161 | 10211181 | 108000 | 21467 | 126710 | 446880 | 3229534 | 4993 | 11500677 | 7563093 | 3900291 | 5712914 | 5787763 | 200692 | 261231.7115 | 10619025 | 9978508 | 1049926 | 1010959 | 1068334 | 1146033 | 1057803 | 1000356 | 900336 | 893942 | 864949 | 855845 | 1054854 | 749300 | 623922 | 440199 | 284583 | 169339 | 142828 | 14358553 | 22771838 | 11608584 | 34.8 | 48504 | 35.8 | 49.5 | 57286 | 33.8 | 89454 | 173734 | 7624216 | 16954978 | 459123 | 454026 | 7616564 | 92692 | 1406614 | 3427384 | 466269 | 37515 | 23827 | 11999638 | 1572787 | 17453939 | 3267049 | 1771782 | 1571022 | 1696027 | 3267049 | 6286745 | 28820 | 28820 | 29707 | 14439 | 14381 | 2062089 | 1969600 | 21626112 | 2091984 | 20371467 | 2234575 | 2085925 | 1990467 | 1812423 | 1801956 | 1753146 | 1762254 | 2071247 | 1564933 | 1323081 | 948699 | 634473 | 399611 | 398711 | 110.8 | 913149 | 28954616 | 3924542 | 4037002 | 1554142 | 257841 | 546026 | 1102103 | 504579 | 298473 | 141680 | 25966 | 3941 | 232406 | 435495 | 27305 | 751696 | 718565 | 539687 | 401342 | 297639 | 111651 | 342816 | 282798 | 522498 | 225754 | 193715 | 190129 | 160088 | 240532 | 1662778 | 199271 | 827432 | 1492651 | 7383 | 1369349 | 27222 | 182557 | 574840 | 1656 | 145357 | 286509 | 195161 | 82795 | 95111 | 17082 | 114932 | 97451 | 70012 | 854973 | 411283 | 295295 | 344420 | 112278 | 4120 | 700747 | 410891 | 133118 | 94734 | 161445 | 243184 | 227922 | 183344 | 38365 | 84691 | 621868 | 44070 | 24312 | 72359 | 201481 | 199469 | 60997 | 21626112 | 124524 | 236631 | 510390 | 229203 | 148579 | 78268 | 11804 | 1715 | 123382 | 270425 | 15874 | 334179 | 322524 | 256069 | 194257 | 156003 | 54214 | 166876 | 141026 | 242077 | 106842 | 94754 | 92548 | 81196 | 118859 | 746527 | 76855 | 359005 | 603599 | 3263 | 582375 | 10984 | 106059 | 311328 | 831 | 73537 | 158365 | 91562 | 43855 | 55012 | 10078 | 56954 | 56138 | 36794 | 392512 | 198958 | 135159 | 153282 | 47834 | 2198 | 438819 | 218771 | 73760 | 51764 | 73393 | 116283 | 113907 | 88717 | 18279 | 32041 | 289865 | 21319 | 12423 | 18523 | 95654 | 64280 | 157 | 10211287 | 1249327 | 785312 | 392300 | 373927 | 178457 | 215169 | 344348 | 912772 | 712376 | 736276 | 2371624 | 752666 | 258831 | 670984 | 2543938 | 1519018 | 3382976 | 702334 | 1391880 | 10211287 | 11236543 | 28954616 | 27 | 4.8 | 671234 | 1025256 | 561071 | 985102 | 66551 | 15750 | 527935 | 20493 | 1095680 | 94609 | 6285595 | 19562731 | 19562731 | 9877750 | 9684981 | 1165365 |
| 2 | 5005 | 1774868 | 494056143933 | 721252426439 | 733561461654 | 5780147040071 | 57206 | 55978 | 113184 | 48359.759399 | 1774868 | 920657 | 854211 | 1234468 | 65662 | 3.27 | 2.59 | 95857 | 768205 | 451725 | 4524627 | 2844992 | 3202872 | 3202872 | 1720637 | 1482235 | 10297331 | 13980509 | 9750809 | 4711535 | 15422531 | 541010 | 622797 | 8316966 | 692667 | 7896904 | 736087 | 711718 | 665428 | 617325 | 653985 | 691792 | 725143 | 555213 | 671105 | 583941 | 447188 | 321376 | 227864 | 297383 | 10347030 | 556396 | 439969 | 20070143 | 4071725 | 4262278 | 575494 | 2207562 | 3920105 | 19494649 | 910026 | 562411 | 659740 | 628211 | 822195 | 1168671 | 908502 | 7524226 | 58228 | 16280 | 331729 | 299313 | 1562083 | 3773 | 3920105 | 1648699 | 3075758 | 1994498 | 1925607 | 113184 | 47126.3986 | 7603630 | 7176760 | 563187 | 645491 | 694720 | 735814 | 699391 | 649639 | 591431 | 624744 | 651555 | 664052 | 578913 | 595536 | 496269 | 361798 | 245015 | 158479 | 156451 | 9723113 | 16636184 | 7732110 | 39.0 | 51058 | 40.5 | 53.2 | 63751 | 37.6 | 87167 | 322649 | 5071787 | 9104863 | 369440 | 340441 | 6469341 | 54956 | 1758588 | 2871143 | 410568 | 83316 | 6187 | 10965280 | 866788 | 16150038 | 1645399 | 1699757 | 825459 | 819940 | 1645399 | 3928983 | 9960 | 9960 | 36550 | 5029 | 4931 | 1104197 | 1268288 | 15920596 | 1387387 | 15073664 | 1471901 | 1411109 | 1315067 | 1208756 | 1278729 | 1343347 | 1389195 | 1134126 | 1266641 | 1080210 | 808986 | 566391 | 386343 | 453834 | 425.9 | 709881 | 20070143 | 3595416 | 2182364 | 1012181 | 512090 | 105026 | 4623 | 566085 | 222285 | 250800 | 1375859 | 372083 | 36331 | 199329 | 565803 | 94595 | 113012 | 39543 | 373383 | 232188 | 170661 | 324706 | 344500 | 271067 | 329966 | 269307 | 19946 | 113275 | 239738 | 665 | 189396 | 20342 | 1016 | 1005 | 2888 | 696353 | 107184 | 38266 | 1333128 | 73019 | 173289 | 194976 | 25993 | 345965 | 23485 | 24115 | 145463 | 144351 | 204688 | 104636 | 1156 | 28446 | 11960 | 1150953 | 23619 | 17471 | 149676 | 107735 | 83723 | 278978 | 126444 | 50178 | 647696 | 52872 | 63074 | 73315 | 1056586 | 17171 | 106449 | 111054 | 39680 | 15920596 | 227930 | 45156 | 1971 | 245731 | 101807 | 129891 | 578041 | 153655 | 19294 | 114087 | 297684 | 42464 | 49670 | 19702 | 178348 | 115820 | 85493 | 169528 | 177703 | 127239 | 155409 | 132719 | 9562 | 57642 | 120941 | 284 | 63030 | 8326 | 352 | 535 | 1156 | 332041 | 60446 | 20774 | 569263 | 37915 | 95602 | 102941 | 13893 | 191240 | 11264 | 12914 | 83122 | 86221 | 98353 | 50421 | 584 | 14648 | 1916 | 521013 | 13514 | 9730 | 84095 | 58870 | 42581 | 150061 | 70282 | 28208 | 261620 | 17305 | 24745 | 34054 | 494486 | 4732 | 41867 | 10397 | 263 | 7524399 | 622595 | 687222 | 291132 | 832210 | 56996 | 880881 | 853619 | 111836 | 726892 | 603512 | 73683 | 1218982 | 398654 | 2497180 | 1108306 | 940871 | 2104912 | 239022 | 633845 | 7524399 | 8391638 | 20070143 | 7 | 5.3 | 546522 | 867239 | 152062 | 379625 | 179685 | 56246 | 246121 | 99717 | 374729 | 263656 | 3926227 | 12613979 | 12613979 | 6448602 | 6165377 | 977829 |
| 3 | 4133 | 585936 | 487005072197 | 613723856820 | 624450149363 | 6548612367402 | 40665 | 42463 | 83128 | 56491.338012 | 585936 | 316402 | 269534 | 1468305 | 59737 | 3.03 | 2.51 | 75281 | 803267 | 282862 | 5125816 | 2763015 | 3423250 | 3423250 | 1775199 | 1648051 | 9939670 | 14984858 | 9428446 | 5273287 | 15994410 | 540535 | 575794 | 8672285 | 647413 | 8288558 | 693040 | 652863 | 622949 | 602161 | 654907 | 696088 | 756439 | 556332 | 740359 | 711079 | 574407 | 426317 | 297361 | 348670 | 10667264 | 630616 | 434241 | 20875686 | 4066365 | 4142104 | 433652 | 1591194 | 5399203 | 20442034 | 951439 | 366646 | 868205 | 873200 | 1196903 | 1521148 | 970483 | 8152474 | 30738 | 13113 | 201778 | 241103 | 848135 | 3477 | 5399203 | 4060859 | 3663685 | 2732325 | 2666878 | 83128 | 53624.7587 | 8132434 | 7737236 | 561572 | 604324 | 667070 | 728403 | 671610 | 629910 | 591323 | 640640 | 666976 | 693978 | 574960 | 651105 | 617390 | 497457 | 358273 | 236773 | 224380 | 10208422 | 17479459 | 8404539 | 42.3 | 43866 | 43.9 | 55.4 | 52098 | 40.6 | 98640 | 212954 | 4819710 | 9694483 | 320999 | 306853 | 5579880 | 52390 | 572823 | 3221472 | 386749 | 50104 | 11742 | 11181203 | 770061 | 15476483 | 898239 | 2287450 | 433750 | 464489 | 898239 | 5193134 | 15219 | 15219 | 29913 | 7485 | 7734 | 1102107 | 1180118 | 16804719 | 1314483 | 16025794 | 1421443 | 1324473 | 1252859 | 1193484 | 1295547 | 1363064 | 1450417 | 1131292 | 1391464 | 1328469 | 1071864 | 784590 | 534134 | 573050 | 389.3 | 627852 | 20875686 | 2959407 | 2990780 | 1107202 | 139564 | 203442 | 252298 | 342663 | 283344 | 129690 | 129485 | 3407 | 78849 | 131796 | 30196 | 479436 | 544452 | 543860 | 408053 | 288055 | 142999 | 417240 | 573770 | 261579 | 85398 | 333616 | 2029 | 115622 | 143470 | 540715 | 227391 | 933973 | 104898 | 70517 | 460679 | 56767 | 240719 | 452235 | 0 | 207742 | 413860 | 143688 | 644510 | 279740 | 779970 | 766863 | 377196 | 100898 | 428649 | 183680 | 8535 | 297341 | 166979 | 48101 | 323008 | 451913 | 233004 | 178840 | 285711 | 217009 | 234897 | 250408 | 90659 | 40210 | 127174 | 79439 | 13767 | 21497 | 103498 | 109161 | 20707 | 16804719 | 64080 | 84617 | 112750 | 150048 | 139641 | 68081 | 53419 | 1152 | 40455 | 79640 | 18059 | 206336 | 237762 | 248795 | 195362 | 149440 | 70108 | 204783 | 289645 | 118846 | 39620 | 162626 | 1011 | 60130 | 70532 | 238120 | 80416 | 393622 | 42245 | 27259 | 185836 | 27665 | 135609 | 234795 | 0 | 103443 | 231898 | 69296 | 347422 | 155361 | 465971 | 402057 | 222582 | 59214 | 190835 | 81384 | 3488 | 138256 | 62390 | 20779 | 187750 | 229070 | 131273 | 90284 | 117945 | 108972 | 124281 | 117164 | 41705 | 15882 | 54240 | 35494 | 7223 | 4930 | 49689 | 35423 | 57 | 8152541 | 551136 | 659156 | 468362 | 154544 | 90042 | 163107 | 111977 | 692893 | 909338 | 452765 | 967498 | 802706 | 1652607 | 229713 | 1692280 | 1574873 | 2924108 | 1011180 | 720330 | 8152541 | 9790195 | 20875686 | 42 | 5.1 | 511224 | 1637654 | 351168 | 819037 | 78889 | 20495 | 557404 | 36949 | 600047 | 111697 | 5192714 | 15242062 | 15242062 | 7772764 | 7469298 | 1199573 |
| 4 | 3210 | 462360 | 315983343213 | 411902114464 | 418518490501 | 4125980999869 | 16036 | 16102 | 32138 | 45285.896438 | 462360 | 240313 | 222047 | 749637 | 61749 | 3.02 | 2.46 | 80493 | 806294 | 236846 | 3167219 | 1763503 | 1470953 | 1470953 | 765013 | 705940 | 6619516 | 9125523 | 6217293 | 3282447 | 9921694 | 335276 | 404682 | 5362136 | 433612 | 5076349 | 410643 | 397480 | 389122 | 375532 | 411895 | 447624 | 487329 | 355242 | 461925 | 408531 | 312098 | 231110 | 173218 | 235227 | 6642616 | 345422 | 268314 | 12992598 | 2573675 | 2724068 | 426311 | 1120165 | 987581 | 12566287 | 549498 | 299343 | 505620 | 485138 | 661013 | 933563 | 649666 | 5117219 | 13841 | 4066 | 72801 | 81708 | 393883 | 1223 | 987581 | 420059 | 2817515 | 484730 | 502851 | 32138 | 44742.7032 | 5010352 | 4726214 | 349790 | 417152 | 441081 | 428491 | 405440 | 392100 | 371081 | 405311 | 435283 | 463856 | 369830 | 429073 | 366389 | 265594 | 180573 | 120018 | 120580 | 6349982 | 10822050 | 5289824 | 41.4 | 46696 | 43.0 | 54.9 | 57362 | 39.8 | 126527 | 185452 | 2988934 | 3115252 | 161589 | 152711 | 3726095 | 18297 | 458294 | 1398152 | 232592 | 16338 | 3998 | 9877346 | 286064 | 12005017 | 410221 | 1270388 | 195431 | 214790 | 410221 | 3463374 | 5221 | 5221 | 32212 | 2622 | 2599 | 685066 | 821834 | 10372488 | 874693 | 9802563 | 839134 | 802920 | 781222 | 746613 | 817206 | 882907 | 951185 | 725072 | 890998 | 774920 | 577692 | 411683 | 293236 | 355807 | 290.4 | 314300 | 12992598 | 1653953 | 1457813 | 585404 | 162479 | 224406 | 22707 | 432943 | 210046 | 93639 | 202612 | 1754 | 84748 | 134652 | 24943 | 212966 | 62172 | 134424 | 548302 | 271213 | 517207 | 239410 | 553292 | 575890 | 903871 | 107575 | 38382 | 88283 | 504795 | 7672 | 14931 | 45556 | 6099 | 2557 | 6112 | 108325 | 136072 | 71952 | 14130 | 273695 | 196055 | 165969 | 5495 | 221381 | 17439 | 19227 | 190704 | 111879 | 144975 | 257849 | 36862 | 11142 | 18502 | 63122 | 23301 | 34327 | 146156 | 125056 | 113432 | 220217 | 317061 | 254390 | 17469 | 0 | 2886 | 271578 | 15924 | 0 | 143081 | 121146 | 29470 | 10372488 | 74277 | 98686 | 10729 | 196059 | 101319 | 45957 | 93730 | 645 | 45424 | 82738 | 12942 | 99476 | 28229 | 66028 | 267265 | 135979 | 258794 | 127103 | 278212 | 269579 | 435594 | 52290 | 18313 | 44342 | 263868 | 3585 | 6189 | 20132 | 2629 | 902 | 2884 | 53850 | 73109 | 39839 | 6765 | 137745 | 110110 | 84780 | 2896 | 116222 | 10022 | 10151 | 101801 | 66453 | 70968 | 124551 | 18223 | 5868 | 6096 | 31133 | 14285 | 17713 | 80722 | 66212 | 56799 | 116780 | 175679 | 129971 | 7816 | 0 | 1255 | 123001 | 7629 | 0 | 57457 | 25114 | 82 | 5117327 | 481070 | 210065 | 479229 | 139701 | 82571 | 185756 | 118011 | 193733 | 1067353 | 1083986 | 36321 | 506198 | 307545 | 287794 | 465570 | 1070547 | 1730882 | 522496 | 1039956 | 5117327 | 5703807 | 12992598 | 22 | 6.1 | 402223 | 586480 | 222750 | 599167 | 27840 | 4755 | 326829 | 6624 | 516090 | 45734 | 3463173 | 10297405 | 10297405 | 5261612 | 5035793 | 725053 |
# Check Datatypes of different columns
g = test_newstate_df.columns.to_series().groupby(test_newstate_df.dtypes).groups
g
{dtype('int64'): Index(['AAGEBASECY', 'AGGDI_CY', 'AGGHINC_CY', 'AGGINC_CY', 'AGGNW_CY',
'AIFBASE_CY', 'AIMBASE_CY', 'AMERIND_CY', 'ASIAN_CY', 'ASNFBASECY',
...
'VAL250K_CY', 'VAL2M_CY', 'VAL50K_CY', 'VAL750K_CY', 'VALBASE_CY',
'WAGEBASECY', 'WHITE_CY', 'WHTFBASECY', 'WHTMBASECY', 'WIDOWED_CY'],
dtype='object', length=326),
dtype('float64'): Index(['AREA', 'AVGFMSZ_CY', 'AVGHHSZ_CY', 'LANDAREA', 'MEDAGE_CY',
'MEDFAGE_CY', 'MEDHHR_CY', 'MEDMAGE_CY', 'POPDENS_CY', 'UNEMPRT_CY'],
dtype='object'),
dtype('O'): Index(['Provider_Count'], dtype='object')}
# Change Provider Count to Float
test_newstate_df['Provider_Count'] = test_newstate_df['Provider_Count'].astype(float)
test_newstate_df.dtypes
Provider_Count float64
AAGEBASECY int64
AGGDI_CY int64
AGGHINC_CY int64
AGGINC_CY int64
AGGNW_CY int64
AIFBASE_CY int64
AIMBASE_CY int64
AMERIND_CY int64
AREA float64
ASIAN_CY int64
ASNFBASECY int64
ASNMBASECY int64
ASSCDEG_CY int64
AVGDI_CY int64
AVGFMSZ_CY float64
AVGHHSZ_CY float64
AVGHINC_CY int64
AVGNW_CY int64
AVGVAL_CY int64
BABYBOOMCY int64
BACHDEG_CY int64
BAGEBASECY int64
BLACK_CY int64
BLKFBASECY int64
BLKMBASECY int64
CIVLBFR_CY int64
EDUCBASECY int64
EMP_CY int64
FAMHH_CY int64
...
THHGRPL7 int64
THHGRPL8 int64
THHGRPL9 int64
THHGRPU1 int64
THHGRPU2 int64
THHGRPU3 int64
THHGRPU4 int64
THHGRPU5 int64
THHGRPU6 int64
TOTHH_CY int64
TOTHU_CY int64
TOTPOP_CY int64
TSEGNUM int64
UNEMPRT_CY float64
UNEMP_CY int64
VACANT_CY int64
VAL0_CY int64
VAL150K_CY int64
VAL1M_CY int64
VAL1PT5MCY int64
VAL250K_CY int64
VAL2M_CY int64
VAL50K_CY int64
VAL750K_CY int64
VALBASE_CY int64
WAGEBASECY int64
WHITE_CY int64
WHTFBASECY int64
WHTMBASECY int64
WIDOWED_CY int64
Length: 337, dtype: object
test_newstate_df.head()
| Provider_Count | AAGEBASECY | AGGDI_CY | AGGHINC_CY | AGGINC_CY | AGGNW_CY | AIFBASE_CY | AIMBASE_CY | AMERIND_CY | AREA | ASIAN_CY | ASNFBASECY | ASNMBASECY | ASSCDEG_CY | AVGDI_CY | AVGFMSZ_CY | AVGHHSZ_CY | AVGHINC_CY | AVGNW_CY | AVGVAL_CY | BABYBOOMCY | BACHDEG_CY | BAGEBASECY | BLACK_CY | BLKFBASECY | BLKMBASECY | CIVLBFR_CY | EDUCBASECY | EMP_CY | FAMHH_CY | FAMPOP_CY | FEM0_CY | FEM15_CY | FEM18UP_CY | FEM20_CY | FEM21UP_CY | FEM25_CY | FEM30_CY | FEM35_CY | FEM40_CY | FEM45_CY | FEM50_CY | FEM55_CY | FEM5_CY | FEM60_CY | FEM65_CY | FEM70_CY | FEM75_CY | FEM80_CY | FEM85_CY | FEMALES_CY | GED_CY | GENALPHACY | GENBASE_CY | GENX_CY | GENZ_CY | GQPOP_CY | GRADDEG_CY | HAGEBASECY | HHPOP_CY | HINC0_CY | HINC150_CY | HINC15_CY | HINC25_CY | HINC35_CY | HINC50_CY | HINC75_CY | HINCBASECY | HISPAI_CY | HISPASN_CY | HISPBLK_CY | HISPMLT_CY | HISPOTH_CY | HISPPI_CY | HISPPOP_CY | HISPWHT_CY | HSGRAD_CY | HSPFBASECY | HSPMBASECY | IAGEBASECY | LANDAREA | MAL18UP_CY | MAL21UP_CY | MALE0_CY | MALE15_CY | MALE20_CY | MALE25_CY | MALE30_CY | MALE35_CY | MALE40_CY | MALE45_CY | MALE50_CY | MALE55_CY | MALE5_CY | MALE60_CY | MALE65_CY | MALE70_CY | MALE75_CY | MALE80_CY | MALE85_CY | MALES_CY | MARBASE_CY | MARRIED_CY | MEDAGE_CY | MEDDI_CY | MEDFAGE_CY | MEDHHR_CY | MEDHINC_CY | MEDMAGE_CY | MEDNW_CY | MEDVAL_CY | MILLENN_CY | MINORITYCY | MLTFBASECY | MLTMBASECY | NEVMARR_CY | NHSPAI_CY | NHSPASN_CY | NHSPBLK_CY | NHSPMLT_CY | NHSPOTH_CY | NHSPPI_CY | NHSPWHT_CY | NOHS_CY | NONHISP_CY | OAGEBASECY | OLDRGENSCY | OTHFBASECY | OTHMBASECY | OTHRACE_CY | OWNER_CY | PACIFIC_CY | PAGEBASECY | PCI_CY | PIFBASE_CY | PIMBASE_CY | POP0_CY | POP15_CY | POP18UP_CY | POP20_CY | POP21UP_CY | POP25_CY | POP30_CY | POP35_CY | POP40_CY | POP45_CY | POP50_CY | POP55_CY | POP5_CY | POP60_CY | POP65_CY | POP70_CY | POP75_CY | POP80_CY | POP85_CY | POPDENS_CY | RACE2UP_CY | RACEBASECY | RENTER_CY | SMCOLL_CY | SOMEHS_CY | TADULT01 | TADULT02 | TADULT03 | TADULT04 | TADULT05 | TADULT06 | TADULT07 | TADULT08 | TADULT09 | TADULT11 | TADULT12 | TADULT13 | TADULT14 | TADULT15 | TADULT16 | TADULT17 | TADULT18 | TADULT19 | TADULT20 | TADULT21 | TADULT22 | TADULT23 | TADULT24 | TADULT25 | TADULT26 | TADULT27 | TADULT28 | TADULT29 | TADULT30 | TADULT31 | TADULT32 | TADULT33 | TADULT34 | TADULT35 | TADULT36 | TADULT37 | TADULT38 | TADULT39 | TADULT40 | TADULT41 | TADULT42 | TADULT43 | TADULT44 | TADULT45 | TADULT46 | TADULT47 | TADULT48 | TADULT49 | TADULT50 | TADULT51 | TADULT52 | TADULT53 | TADULT54 | TADULT55 | TADULT56 | TADULT57 | TADULT58 | TADULT59 | TADULT60 | TADULT61 | TADULT62 | TADULT63 | TADULT64 | TADULT65 | TADULT66 | TADULT67 | TADULT68 | TADULTBASE | THH01 | THH02 | THH03 | THH04 | THH05 | THH06 | THH07 | THH08 | THH09 | THH11 | THH12 | THH13 | THH14 | THH15 | THH16 | THH17 | THH18 | THH19 | THH20 | THH21 | THH22 | THH23 | THH24 | THH25 | THH26 | THH27 | THH28 | THH29 | THH30 | THH31 | THH32 | THH33 | THH34 | THH35 | THH36 | THH37 | THH38 | THH39 | THH40 | THH41 | THH42 | THH43 | THH44 | THH45 | THH46 | THH47 | THH48 | THH49 | THH50 | THH51 | THH52 | THH53 | THH54 | THH55 | THH56 | THH57 | THH58 | THH59 | THH60 | THH61 | THH62 | THH63 | THH64 | THH65 | THH66 | THH67 | THH68 | THHBASE | THHGRPL1 | THHGRPL11 | THHGRPL12 | THHGRPL13 | THHGRPL14 | THHGRPL2 | THHGRPL3 | THHGRPL4 | THHGRPL5 | THHGRPL6 | THHGRPL7 | THHGRPL8 | THHGRPL9 | THHGRPU1 | THHGRPU2 | THHGRPU3 | THHGRPU4 | THHGRPU5 | THHGRPU6 | TOTHH_CY | TOTHU_CY | TOTPOP_CY | TSEGNUM | UNEMPRT_CY | UNEMP_CY | VACANT_CY | VAL0_CY | VAL150K_CY | VAL1M_CY | VAL1PT5MCY | VAL250K_CY | VAL2M_CY | VAL50K_CY | VAL750K_CY | VALBASE_CY | WAGEBASECY | WHITE_CY | WHTFBASECY | WHTMBASECY | WIDOWED_CY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7353.0 | 5808539 | 984770070408 | 1341852609490 | 1363545956974 | 10953288740347 | 186300 | 188412 | 374712 | 158042.920912 | 5808539 | 3057816 | 2750723 | 2056384 | 73844 | 3.50 | 2.92 | 100620 | 821339 | 637422 | 8055682 | 5552919 | 2345048 | 2345048 | 1186331 | 1158717 | 19595308 | 26629106 | 18678853 | 9166028 | 32063051 | 1246195 | 1267704 | 15535129 | 1399534 | 14709444 | 1533458 | 1442776 | 1332317 | 1220853 | 1252603 | 1262625 | 1290328 | 1247274 | 1173091 | 1008278 | 746364 | 527232 | 362402 | 447803 | 20024285 | 621048 | 1008060 | 39806791 | 8011720 | 9289070 | 813593 | 3342632 | 15757962 | 38993198 | 1280615 | 1084396 | 1102410 | 1036596 | 1486913 | 2149782 | 1630222 | 13335897 | 214901 | 94837 | 149373 | 953706 | 7017528 | 17243 | 15757962 | 7310374 | 4804568 | 7811593 | 7946369 | 374712 | 155779.2198 | 15076422 | 14203971 | 1298461 | 1348980 | 1481021 | 1618901 | 1503701 | 1378564 | 1227529 | 1251002 | 1245261 | 1228175 | 1301504 | 1076510 | 891235 | 637267 | 427533 | 272897 | 270401 | 19782506 | 32126345 | 15627854 | 36.2 | 56020 | 37.3 | 51.5 | 69051 | 35.1 | 99297 | 505800 | 10636136 | 25228888 | 1076167 | 1053726 | 11926897 | 159811 | 5713702 | 2195675 | 1176187 | 84542 | 141009 | 14577903 | 2557052 | 24048829 | 7102070 | 2806123 | 3484353 | 3617717 | 7102070 | 7294468 | 158252 | 158252 | 34254 | 79878 | 78374 | 2544656 | 2616684 | 30611551 | 2880555 | 28913415 | 3152359 | 2946477 | 2710881 | 2448382 | 2503605 | 2507886 | 2518503 | 2548778 | 2249601 | 1899513 | 1383631 | 954765 | 635299 | 718204 | 255.5 | 2129893 | 39806791 | 6041636 | 5627462 | 2067041 | 962752 | 447008 | 877339 | 707623 | 1030736 | 998289 | 1614673 | 1324877 | 854053 | 428180 | 1051926 | 640890 | 507501 | 147970 | 237918 | 290669 | 232487 | 111099 | 175840 | 175248 | 27484 | 508845 | 34216 | 94745 | 26920 | 657657 | 2654588 | 898660 | 530885 | 587455 | 321133 | 1177243 | 208733 | 337293 | 394628 | 476335 | 299210 | 59480 | 140493 | 366232 | 156521 | 289040 | 189701 | 202710 | 74475 | 54655 | 10628 | 210357 | 24920 | 79246 | 244837 | 409205 | 166215 | 21790 | 118065 | 25748 | 113377 | 9029 | 1432303 | 1875298 | 534518 | 504305 | 79548 | 103401 | 172871 | 214308 | 121327 | 30611551 | 435771 | 183824 | 367891 | 297569 | 476909 | 507339 | 673663 | 508597 | 428604 | 259020 | 577307 | 263847 | 213335 | 66627 | 109074 | 143993 | 108873 | 52243 | 85167 | 78061 | 12137 | 244469 | 15552 | 49244 | 13249 | 260783 | 911215 | 362738 | 195394 | 210403 | 128040 | 543776 | 111747 | 169541 | 165386 | 226491 | 161769 | 28909 | 76061 | 193740 | 98928 | 146079 | 107181 | 112822 | 31553 | 22139 | 5084 | 95731 | 9515 | 37825 | 135236 | 200618 | 89406 | 11253 | 50951 | 12199 | 59308 | 4548 | 605661 | 641139 | 209939 | 214632 | 38327 | 25756 | 76824 | 50008 | 179 | 13336104 | 1761964 | 474338 | 127006 | 1709698 | 152588 | 2118203 | 1161232 | 543809 | 499350 | 412712 | 2068573 | 1407619 | 734811 | 1827341 | 4374461 | 1267838 | 4807134 | 591397 | 467754 | 13336104 | 14383561 | 39806791 | 28 | 4.7 | 916455 | 1047457 | 190476 | 277000 | 678744 | 195039 | 432916 | 242837 | 160723 | 903689 | 7293065 | 21888277 | 21888277 | 10953440 | 10934837 | 1601462 |
| 1 | 5115.0 | 1428081 | 663797428676 | 846781778790 | 860166554925 | 6544101733836 | 99642 | 101050 | 200692 | 264622.431161 | 1428081 | 736438 | 691643 | 1352089 | 65007 | 3.36 | 2.78 | 82927 | 640876 | 234140 | 5610338 | 3595450 | 3554094 | 3554094 | 1837649 | 1716445 | 13994294 | 18710254 | 13323060 | 7102474 | 23868426 | 1012163 | 958641 | 11007087 | 1023650 | 10392959 | 1088542 | 1028122 | 990111 | 912087 | 908014 | 888197 | 906409 | 1016393 | 815633 | 699159 | 508500 | 349890 | 230272 | 255883 | 14596063 | 796406 | 820030 | 28954616 | 5795902 | 7332348 | 603396 | 1902087 | 11500677 | 28351220 | 1099136 | 601595 | 972153 | 998707 | 1339610 | 1837748 | 1220161 | 10211181 | 108000 | 21467 | 126710 | 446880 | 3229534 | 4993 | 11500677 | 7563093 | 3900291 | 5712914 | 5787763 | 200692 | 261231.7115 | 10619025 | 9978508 | 1049926 | 1010959 | 1068334 | 1146033 | 1057803 | 1000356 | 900336 | 893942 | 864949 | 855845 | 1054854 | 749300 | 623922 | 440199 | 284583 | 169339 | 142828 | 14358553 | 22771838 | 11608584 | 34.8 | 48504 | 35.8 | 49.5 | 57286 | 33.8 | 89454 | 173734 | 7624216 | 16954978 | 459123 | 454026 | 7616564 | 92692 | 1406614 | 3427384 | 466269 | 37515 | 23827 | 11999638 | 1572787 | 17453939 | 3267049 | 1771782 | 1571022 | 1696027 | 3267049 | 6286745 | 28820 | 28820 | 29707 | 14439 | 14381 | 2062089 | 1969600 | 21626112 | 2091984 | 20371467 | 2234575 | 2085925 | 1990467 | 1812423 | 1801956 | 1753146 | 1762254 | 2071247 | 1564933 | 1323081 | 948699 | 634473 | 399611 | 398711 | 110.8 | 913149 | 28954616 | 3924542 | 4037002 | 1554142 | 257841 | 546026 | 1102103 | 504579 | 298473 | 141680 | 25966 | 3941 | 232406 | 435495 | 27305 | 751696 | 718565 | 539687 | 401342 | 297639 | 111651 | 342816 | 282798 | 522498 | 225754 | 193715 | 190129 | 160088 | 240532 | 1662778 | 199271 | 827432 | 1492651 | 7383 | 1369349 | 27222 | 182557 | 574840 | 1656 | 145357 | 286509 | 195161 | 82795 | 95111 | 17082 | 114932 | 97451 | 70012 | 854973 | 411283 | 295295 | 344420 | 112278 | 4120 | 700747 | 410891 | 133118 | 94734 | 161445 | 243184 | 227922 | 183344 | 38365 | 84691 | 621868 | 44070 | 24312 | 72359 | 201481 | 199469 | 60997 | 21626112 | 124524 | 236631 | 510390 | 229203 | 148579 | 78268 | 11804 | 1715 | 123382 | 270425 | 15874 | 334179 | 322524 | 256069 | 194257 | 156003 | 54214 | 166876 | 141026 | 242077 | 106842 | 94754 | 92548 | 81196 | 118859 | 746527 | 76855 | 359005 | 603599 | 3263 | 582375 | 10984 | 106059 | 311328 | 831 | 73537 | 158365 | 91562 | 43855 | 55012 | 10078 | 56954 | 56138 | 36794 | 392512 | 198958 | 135159 | 153282 | 47834 | 2198 | 438819 | 218771 | 73760 | 51764 | 73393 | 116283 | 113907 | 88717 | 18279 | 32041 | 289865 | 21319 | 12423 | 18523 | 95654 | 64280 | 157 | 10211287 | 1249327 | 785312 | 392300 | 373927 | 178457 | 215169 | 344348 | 912772 | 712376 | 736276 | 2371624 | 752666 | 258831 | 670984 | 2543938 | 1519018 | 3382976 | 702334 | 1391880 | 10211287 | 11236543 | 28954616 | 27 | 4.8 | 671234 | 1025256 | 561071 | 985102 | 66551 | 15750 | 527935 | 20493 | 1095680 | 94609 | 6285595 | 19562731 | 19562731 | 9877750 | 9684981 | 1165365 |
| 2 | 5005.0 | 1774868 | 494056143933 | 721252426439 | 733561461654 | 5780147040071 | 57206 | 55978 | 113184 | 48359.759399 | 1774868 | 920657 | 854211 | 1234468 | 65662 | 3.27 | 2.59 | 95857 | 768205 | 451725 | 4524627 | 2844992 | 3202872 | 3202872 | 1720637 | 1482235 | 10297331 | 13980509 | 9750809 | 4711535 | 15422531 | 541010 | 622797 | 8316966 | 692667 | 7896904 | 736087 | 711718 | 665428 | 617325 | 653985 | 691792 | 725143 | 555213 | 671105 | 583941 | 447188 | 321376 | 227864 | 297383 | 10347030 | 556396 | 439969 | 20070143 | 4071725 | 4262278 | 575494 | 2207562 | 3920105 | 19494649 | 910026 | 562411 | 659740 | 628211 | 822195 | 1168671 | 908502 | 7524226 | 58228 | 16280 | 331729 | 299313 | 1562083 | 3773 | 3920105 | 1648699 | 3075758 | 1994498 | 1925607 | 113184 | 47126.3986 | 7603630 | 7176760 | 563187 | 645491 | 694720 | 735814 | 699391 | 649639 | 591431 | 624744 | 651555 | 664052 | 578913 | 595536 | 496269 | 361798 | 245015 | 158479 | 156451 | 9723113 | 16636184 | 7732110 | 39.0 | 51058 | 40.5 | 53.2 | 63751 | 37.6 | 87167 | 322649 | 5071787 | 9104863 | 369440 | 340441 | 6469341 | 54956 | 1758588 | 2871143 | 410568 | 83316 | 6187 | 10965280 | 866788 | 16150038 | 1645399 | 1699757 | 825459 | 819940 | 1645399 | 3928983 | 9960 | 9960 | 36550 | 5029 | 4931 | 1104197 | 1268288 | 15920596 | 1387387 | 15073664 | 1471901 | 1411109 | 1315067 | 1208756 | 1278729 | 1343347 | 1389195 | 1134126 | 1266641 | 1080210 | 808986 | 566391 | 386343 | 453834 | 425.9 | 709881 | 20070143 | 3595416 | 2182364 | 1012181 | 512090 | 105026 | 4623 | 566085 | 222285 | 250800 | 1375859 | 372083 | 36331 | 199329 | 565803 | 94595 | 113012 | 39543 | 373383 | 232188 | 170661 | 324706 | 344500 | 271067 | 329966 | 269307 | 19946 | 113275 | 239738 | 665 | 189396 | 20342 | 1016 | 1005 | 2888 | 696353 | 107184 | 38266 | 1333128 | 73019 | 173289 | 194976 | 25993 | 345965 | 23485 | 24115 | 145463 | 144351 | 204688 | 104636 | 1156 | 28446 | 11960 | 1150953 | 23619 | 17471 | 149676 | 107735 | 83723 | 278978 | 126444 | 50178 | 647696 | 52872 | 63074 | 73315 | 1056586 | 17171 | 106449 | 111054 | 39680 | 15920596 | 227930 | 45156 | 1971 | 245731 | 101807 | 129891 | 578041 | 153655 | 19294 | 114087 | 297684 | 42464 | 49670 | 19702 | 178348 | 115820 | 85493 | 169528 | 177703 | 127239 | 155409 | 132719 | 9562 | 57642 | 120941 | 284 | 63030 | 8326 | 352 | 535 | 1156 | 332041 | 60446 | 20774 | 569263 | 37915 | 95602 | 102941 | 13893 | 191240 | 11264 | 12914 | 83122 | 86221 | 98353 | 50421 | 584 | 14648 | 1916 | 521013 | 13514 | 9730 | 84095 | 58870 | 42581 | 150061 | 70282 | 28208 | 261620 | 17305 | 24745 | 34054 | 494486 | 4732 | 41867 | 10397 | 263 | 7524399 | 622595 | 687222 | 291132 | 832210 | 56996 | 880881 | 853619 | 111836 | 726892 | 603512 | 73683 | 1218982 | 398654 | 2497180 | 1108306 | 940871 | 2104912 | 239022 | 633845 | 7524399 | 8391638 | 20070143 | 7 | 5.3 | 546522 | 867239 | 152062 | 379625 | 179685 | 56246 | 246121 | 99717 | 374729 | 263656 | 3926227 | 12613979 | 12613979 | 6448602 | 6165377 | 977829 |
| 3 | 4133.0 | 585936 | 487005072197 | 613723856820 | 624450149363 | 6548612367402 | 40665 | 42463 | 83128 | 56491.338012 | 585936 | 316402 | 269534 | 1468305 | 59737 | 3.03 | 2.51 | 75281 | 803267 | 282862 | 5125816 | 2763015 | 3423250 | 3423250 | 1775199 | 1648051 | 9939670 | 14984858 | 9428446 | 5273287 | 15994410 | 540535 | 575794 | 8672285 | 647413 | 8288558 | 693040 | 652863 | 622949 | 602161 | 654907 | 696088 | 756439 | 556332 | 740359 | 711079 | 574407 | 426317 | 297361 | 348670 | 10667264 | 630616 | 434241 | 20875686 | 4066365 | 4142104 | 433652 | 1591194 | 5399203 | 20442034 | 951439 | 366646 | 868205 | 873200 | 1196903 | 1521148 | 970483 | 8152474 | 30738 | 13113 | 201778 | 241103 | 848135 | 3477 | 5399203 | 4060859 | 3663685 | 2732325 | 2666878 | 83128 | 53624.7587 | 8132434 | 7737236 | 561572 | 604324 | 667070 | 728403 | 671610 | 629910 | 591323 | 640640 | 666976 | 693978 | 574960 | 651105 | 617390 | 497457 | 358273 | 236773 | 224380 | 10208422 | 17479459 | 8404539 | 42.3 | 43866 | 43.9 | 55.4 | 52098 | 40.6 | 98640 | 212954 | 4819710 | 9694483 | 320999 | 306853 | 5579880 | 52390 | 572823 | 3221472 | 386749 | 50104 | 11742 | 11181203 | 770061 | 15476483 | 898239 | 2287450 | 433750 | 464489 | 898239 | 5193134 | 15219 | 15219 | 29913 | 7485 | 7734 | 1102107 | 1180118 | 16804719 | 1314483 | 16025794 | 1421443 | 1324473 | 1252859 | 1193484 | 1295547 | 1363064 | 1450417 | 1131292 | 1391464 | 1328469 | 1071864 | 784590 | 534134 | 573050 | 389.3 | 627852 | 20875686 | 2959407 | 2990780 | 1107202 | 139564 | 203442 | 252298 | 342663 | 283344 | 129690 | 129485 | 3407 | 78849 | 131796 | 30196 | 479436 | 544452 | 543860 | 408053 | 288055 | 142999 | 417240 | 573770 | 261579 | 85398 | 333616 | 2029 | 115622 | 143470 | 540715 | 227391 | 933973 | 104898 | 70517 | 460679 | 56767 | 240719 | 452235 | 0 | 207742 | 413860 | 143688 | 644510 | 279740 | 779970 | 766863 | 377196 | 100898 | 428649 | 183680 | 8535 | 297341 | 166979 | 48101 | 323008 | 451913 | 233004 | 178840 | 285711 | 217009 | 234897 | 250408 | 90659 | 40210 | 127174 | 79439 | 13767 | 21497 | 103498 | 109161 | 20707 | 16804719 | 64080 | 84617 | 112750 | 150048 | 139641 | 68081 | 53419 | 1152 | 40455 | 79640 | 18059 | 206336 | 237762 | 248795 | 195362 | 149440 | 70108 | 204783 | 289645 | 118846 | 39620 | 162626 | 1011 | 60130 | 70532 | 238120 | 80416 | 393622 | 42245 | 27259 | 185836 | 27665 | 135609 | 234795 | 0 | 103443 | 231898 | 69296 | 347422 | 155361 | 465971 | 402057 | 222582 | 59214 | 190835 | 81384 | 3488 | 138256 | 62390 | 20779 | 187750 | 229070 | 131273 | 90284 | 117945 | 108972 | 124281 | 117164 | 41705 | 15882 | 54240 | 35494 | 7223 | 4930 | 49689 | 35423 | 57 | 8152541 | 551136 | 659156 | 468362 | 154544 | 90042 | 163107 | 111977 | 692893 | 909338 | 452765 | 967498 | 802706 | 1652607 | 229713 | 1692280 | 1574873 | 2924108 | 1011180 | 720330 | 8152541 | 9790195 | 20875686 | 42 | 5.1 | 511224 | 1637654 | 351168 | 819037 | 78889 | 20495 | 557404 | 36949 | 600047 | 111697 | 5192714 | 15242062 | 15242062 | 7772764 | 7469298 | 1199573 |
| 4 | 3210.0 | 462360 | 315983343213 | 411902114464 | 418518490501 | 4125980999869 | 16036 | 16102 | 32138 | 45285.896438 | 462360 | 240313 | 222047 | 749637 | 61749 | 3.02 | 2.46 | 80493 | 806294 | 236846 | 3167219 | 1763503 | 1470953 | 1470953 | 765013 | 705940 | 6619516 | 9125523 | 6217293 | 3282447 | 9921694 | 335276 | 404682 | 5362136 | 433612 | 5076349 | 410643 | 397480 | 389122 | 375532 | 411895 | 447624 | 487329 | 355242 | 461925 | 408531 | 312098 | 231110 | 173218 | 235227 | 6642616 | 345422 | 268314 | 12992598 | 2573675 | 2724068 | 426311 | 1120165 | 987581 | 12566287 | 549498 | 299343 | 505620 | 485138 | 661013 | 933563 | 649666 | 5117219 | 13841 | 4066 | 72801 | 81708 | 393883 | 1223 | 987581 | 420059 | 2817515 | 484730 | 502851 | 32138 | 44742.7032 | 5010352 | 4726214 | 349790 | 417152 | 441081 | 428491 | 405440 | 392100 | 371081 | 405311 | 435283 | 463856 | 369830 | 429073 | 366389 | 265594 | 180573 | 120018 | 120580 | 6349982 | 10822050 | 5289824 | 41.4 | 46696 | 43.0 | 54.9 | 57362 | 39.8 | 126527 | 185452 | 2988934 | 3115252 | 161589 | 152711 | 3726095 | 18297 | 458294 | 1398152 | 232592 | 16338 | 3998 | 9877346 | 286064 | 12005017 | 410221 | 1270388 | 195431 | 214790 | 410221 | 3463374 | 5221 | 5221 | 32212 | 2622 | 2599 | 685066 | 821834 | 10372488 | 874693 | 9802563 | 839134 | 802920 | 781222 | 746613 | 817206 | 882907 | 951185 | 725072 | 890998 | 774920 | 577692 | 411683 | 293236 | 355807 | 290.4 | 314300 | 12992598 | 1653953 | 1457813 | 585404 | 162479 | 224406 | 22707 | 432943 | 210046 | 93639 | 202612 | 1754 | 84748 | 134652 | 24943 | 212966 | 62172 | 134424 | 548302 | 271213 | 517207 | 239410 | 553292 | 575890 | 903871 | 107575 | 38382 | 88283 | 504795 | 7672 | 14931 | 45556 | 6099 | 2557 | 6112 | 108325 | 136072 | 71952 | 14130 | 273695 | 196055 | 165969 | 5495 | 221381 | 17439 | 19227 | 190704 | 111879 | 144975 | 257849 | 36862 | 11142 | 18502 | 63122 | 23301 | 34327 | 146156 | 125056 | 113432 | 220217 | 317061 | 254390 | 17469 | 0 | 2886 | 271578 | 15924 | 0 | 143081 | 121146 | 29470 | 10372488 | 74277 | 98686 | 10729 | 196059 | 101319 | 45957 | 93730 | 645 | 45424 | 82738 | 12942 | 99476 | 28229 | 66028 | 267265 | 135979 | 258794 | 127103 | 278212 | 269579 | 435594 | 52290 | 18313 | 44342 | 263868 | 3585 | 6189 | 20132 | 2629 | 902 | 2884 | 53850 | 73109 | 39839 | 6765 | 137745 | 110110 | 84780 | 2896 | 116222 | 10022 | 10151 | 101801 | 66453 | 70968 | 124551 | 18223 | 5868 | 6096 | 31133 | 14285 | 17713 | 80722 | 66212 | 56799 | 116780 | 175679 | 129971 | 7816 | 0 | 1255 | 123001 | 7629 | 0 | 57457 | 25114 | 82 | 5117327 | 481070 | 210065 | 479229 | 139701 | 82571 | 185756 | 118011 | 193733 | 1067353 | 1083986 | 36321 | 506198 | 307545 | 287794 | 465570 | 1070547 | 1730882 | 522496 | 1039956 | 5117327 | 5703807 | 12992598 | 22 | 6.1 | 402223 | 586480 | 222750 | 599167 | 27840 | 4755 | 326829 | 6624 | 516090 | 45734 | 3463173 | 10297405 | 10297405 | 5261612 | 5035793 | 725053 |
import seaborn as sns
sns.set(rc={'figure.figsize':(18,8.27)})
# sns.heatmap(statetest3.corr().values, statetest.columns, statetest.columns, sort_f=False)
corr = test_newstate_df.corr()
# sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)
sns.heatmap(corr, vmin=-1.0, vmax=1.0)
<matplotlib.axes._subplots.AxesSubplot at 0x1f4ac6e78d0>
pd.plotting.scatter_matrix(test_newstate_df)
plt.show()
principal component analysis is a method of extracting important variables (in form of components) from a large set of variables available in a data set. It extracts low dimensional set of features from a high dimensional data set with a motive to capture as much information as possible. With fewer variables, visualization also becomes much more meaningful. Let’s say we have a data set of dimension 300 (n) × 50 (p). n represents the number of observations and p represents number of predictors. each resultant dimension is a linear combination of p features. PCA works on numeric variables only. Also the data needs to be normalized. PCA finds the best fit line by maximizing the sum of squared distances from projected points to origin.
test_newstate_df.shape
(51, 337)
test_newstate_df.head()
| Provider_Count | AAGEBASECY | AGGDI_CY | AGGHINC_CY | AGGINC_CY | AGGNW_CY | AIFBASE_CY | AIMBASE_CY | AMERIND_CY | AREA | ASIAN_CY | ASNFBASECY | ASNMBASECY | ASSCDEG_CY | AVGDI_CY | AVGFMSZ_CY | AVGHHSZ_CY | AVGHINC_CY | AVGNW_CY | AVGVAL_CY | BABYBOOMCY | BACHDEG_CY | BAGEBASECY | BLACK_CY | BLKFBASECY | BLKMBASECY | CIVLBFR_CY | EDUCBASECY | EMP_CY | FAMHH_CY | FAMPOP_CY | FEM0_CY | FEM15_CY | FEM18UP_CY | FEM20_CY | FEM21UP_CY | FEM25_CY | FEM30_CY | FEM35_CY | FEM40_CY | FEM45_CY | FEM50_CY | FEM55_CY | FEM5_CY | FEM60_CY | FEM65_CY | FEM70_CY | FEM75_CY | FEM80_CY | FEM85_CY | FEMALES_CY | GED_CY | GENALPHACY | GENBASE_CY | GENX_CY | GENZ_CY | GQPOP_CY | GRADDEG_CY | HAGEBASECY | HHPOP_CY | HINC0_CY | HINC150_CY | HINC15_CY | HINC25_CY | HINC35_CY | HINC50_CY | HINC75_CY | HINCBASECY | HISPAI_CY | HISPASN_CY | HISPBLK_CY | HISPMLT_CY | HISPOTH_CY | HISPPI_CY | HISPPOP_CY | HISPWHT_CY | HSGRAD_CY | HSPFBASECY | HSPMBASECY | IAGEBASECY | LANDAREA | MAL18UP_CY | MAL21UP_CY | MALE0_CY | MALE15_CY | MALE20_CY | MALE25_CY | MALE30_CY | MALE35_CY | MALE40_CY | MALE45_CY | MALE50_CY | MALE55_CY | MALE5_CY | MALE60_CY | MALE65_CY | MALE70_CY | MALE75_CY | MALE80_CY | MALE85_CY | MALES_CY | MARBASE_CY | MARRIED_CY | MEDAGE_CY | MEDDI_CY | MEDFAGE_CY | MEDHHR_CY | MEDHINC_CY | MEDMAGE_CY | MEDNW_CY | MEDVAL_CY | MILLENN_CY | MINORITYCY | MLTFBASECY | MLTMBASECY | NEVMARR_CY | NHSPAI_CY | NHSPASN_CY | NHSPBLK_CY | NHSPMLT_CY | NHSPOTH_CY | NHSPPI_CY | NHSPWHT_CY | NOHS_CY | NONHISP_CY | OAGEBASECY | OLDRGENSCY | OTHFBASECY | OTHMBASECY | OTHRACE_CY | OWNER_CY | PACIFIC_CY | PAGEBASECY | PCI_CY | PIFBASE_CY | PIMBASE_CY | POP0_CY | POP15_CY | POP18UP_CY | POP20_CY | POP21UP_CY | POP25_CY | POP30_CY | POP35_CY | POP40_CY | POP45_CY | POP50_CY | POP55_CY | POP5_CY | POP60_CY | POP65_CY | POP70_CY | POP75_CY | POP80_CY | POP85_CY | POPDENS_CY | RACE2UP_CY | RACEBASECY | RENTER_CY | SMCOLL_CY | SOMEHS_CY | TADULT01 | TADULT02 | TADULT03 | TADULT04 | TADULT05 | TADULT06 | TADULT07 | TADULT08 | TADULT09 | TADULT11 | TADULT12 | TADULT13 | TADULT14 | TADULT15 | TADULT16 | TADULT17 | TADULT18 | TADULT19 | TADULT20 | TADULT21 | TADULT22 | TADULT23 | TADULT24 | TADULT25 | TADULT26 | TADULT27 | TADULT28 | TADULT29 | TADULT30 | TADULT31 | TADULT32 | TADULT33 | TADULT34 | TADULT35 | TADULT36 | TADULT37 | TADULT38 | TADULT39 | TADULT40 | TADULT41 | TADULT42 | TADULT43 | TADULT44 | TADULT45 | TADULT46 | TADULT47 | TADULT48 | TADULT49 | TADULT50 | TADULT51 | TADULT52 | TADULT53 | TADULT54 | TADULT55 | TADULT56 | TADULT57 | TADULT58 | TADULT59 | TADULT60 | TADULT61 | TADULT62 | TADULT63 | TADULT64 | TADULT65 | TADULT66 | TADULT67 | TADULT68 | TADULTBASE | THH01 | THH02 | THH03 | THH04 | THH05 | THH06 | THH07 | THH08 | THH09 | THH11 | THH12 | THH13 | THH14 | THH15 | THH16 | THH17 | THH18 | THH19 | THH20 | THH21 | THH22 | THH23 | THH24 | THH25 | THH26 | THH27 | THH28 | THH29 | THH30 | THH31 | THH32 | THH33 | THH34 | THH35 | THH36 | THH37 | THH38 | THH39 | THH40 | THH41 | THH42 | THH43 | THH44 | THH45 | THH46 | THH47 | THH48 | THH49 | THH50 | THH51 | THH52 | THH53 | THH54 | THH55 | THH56 | THH57 | THH58 | THH59 | THH60 | THH61 | THH62 | THH63 | THH64 | THH65 | THH66 | THH67 | THH68 | THHBASE | THHGRPL1 | THHGRPL11 | THHGRPL12 | THHGRPL13 | THHGRPL14 | THHGRPL2 | THHGRPL3 | THHGRPL4 | THHGRPL5 | THHGRPL6 | THHGRPL7 | THHGRPL8 | THHGRPL9 | THHGRPU1 | THHGRPU2 | THHGRPU3 | THHGRPU4 | THHGRPU5 | THHGRPU6 | TOTHH_CY | TOTHU_CY | TOTPOP_CY | TSEGNUM | UNEMPRT_CY | UNEMP_CY | VACANT_CY | VAL0_CY | VAL150K_CY | VAL1M_CY | VAL1PT5MCY | VAL250K_CY | VAL2M_CY | VAL50K_CY | VAL750K_CY | VALBASE_CY | WAGEBASECY | WHITE_CY | WHTFBASECY | WHTMBASECY | WIDOWED_CY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7353.0 | 5808539 | 984770070408 | 1341852609490 | 1363545956974 | 10953288740347 | 186300 | 188412 | 374712 | 158042.920912 | 5808539 | 3057816 | 2750723 | 2056384 | 73844 | 3.50 | 2.92 | 100620 | 821339 | 637422 | 8055682 | 5552919 | 2345048 | 2345048 | 1186331 | 1158717 | 19595308 | 26629106 | 18678853 | 9166028 | 32063051 | 1246195 | 1267704 | 15535129 | 1399534 | 14709444 | 1533458 | 1442776 | 1332317 | 1220853 | 1252603 | 1262625 | 1290328 | 1247274 | 1173091 | 1008278 | 746364 | 527232 | 362402 | 447803 | 20024285 | 621048 | 1008060 | 39806791 | 8011720 | 9289070 | 813593 | 3342632 | 15757962 | 38993198 | 1280615 | 1084396 | 1102410 | 1036596 | 1486913 | 2149782 | 1630222 | 13335897 | 214901 | 94837 | 149373 | 953706 | 7017528 | 17243 | 15757962 | 7310374 | 4804568 | 7811593 | 7946369 | 374712 | 155779.2198 | 15076422 | 14203971 | 1298461 | 1348980 | 1481021 | 1618901 | 1503701 | 1378564 | 1227529 | 1251002 | 1245261 | 1228175 | 1301504 | 1076510 | 891235 | 637267 | 427533 | 272897 | 270401 | 19782506 | 32126345 | 15627854 | 36.2 | 56020 | 37.3 | 51.5 | 69051 | 35.1 | 99297 | 505800 | 10636136 | 25228888 | 1076167 | 1053726 | 11926897 | 159811 | 5713702 | 2195675 | 1176187 | 84542 | 141009 | 14577903 | 2557052 | 24048829 | 7102070 | 2806123 | 3484353 | 3617717 | 7102070 | 7294468 | 158252 | 158252 | 34254 | 79878 | 78374 | 2544656 | 2616684 | 30611551 | 2880555 | 28913415 | 3152359 | 2946477 | 2710881 | 2448382 | 2503605 | 2507886 | 2518503 | 2548778 | 2249601 | 1899513 | 1383631 | 954765 | 635299 | 718204 | 255.5 | 2129893 | 39806791 | 6041636 | 5627462 | 2067041 | 962752 | 447008 | 877339 | 707623 | 1030736 | 998289 | 1614673 | 1324877 | 854053 | 428180 | 1051926 | 640890 | 507501 | 147970 | 237918 | 290669 | 232487 | 111099 | 175840 | 175248 | 27484 | 508845 | 34216 | 94745 | 26920 | 657657 | 2654588 | 898660 | 530885 | 587455 | 321133 | 1177243 | 208733 | 337293 | 394628 | 476335 | 299210 | 59480 | 140493 | 366232 | 156521 | 289040 | 189701 | 202710 | 74475 | 54655 | 10628 | 210357 | 24920 | 79246 | 244837 | 409205 | 166215 | 21790 | 118065 | 25748 | 113377 | 9029 | 1432303 | 1875298 | 534518 | 504305 | 79548 | 103401 | 172871 | 214308 | 121327 | 30611551 | 435771 | 183824 | 367891 | 297569 | 476909 | 507339 | 673663 | 508597 | 428604 | 259020 | 577307 | 263847 | 213335 | 66627 | 109074 | 143993 | 108873 | 52243 | 85167 | 78061 | 12137 | 244469 | 15552 | 49244 | 13249 | 260783 | 911215 | 362738 | 195394 | 210403 | 128040 | 543776 | 111747 | 169541 | 165386 | 226491 | 161769 | 28909 | 76061 | 193740 | 98928 | 146079 | 107181 | 112822 | 31553 | 22139 | 5084 | 95731 | 9515 | 37825 | 135236 | 200618 | 89406 | 11253 | 50951 | 12199 | 59308 | 4548 | 605661 | 641139 | 209939 | 214632 | 38327 | 25756 | 76824 | 50008 | 179 | 13336104 | 1761964 | 474338 | 127006 | 1709698 | 152588 | 2118203 | 1161232 | 543809 | 499350 | 412712 | 2068573 | 1407619 | 734811 | 1827341 | 4374461 | 1267838 | 4807134 | 591397 | 467754 | 13336104 | 14383561 | 39806791 | 28 | 4.7 | 916455 | 1047457 | 190476 | 277000 | 678744 | 195039 | 432916 | 242837 | 160723 | 903689 | 7293065 | 21888277 | 21888277 | 10953440 | 10934837 | 1601462 |
| 1 | 5115.0 | 1428081 | 663797428676 | 846781778790 | 860166554925 | 6544101733836 | 99642 | 101050 | 200692 | 264622.431161 | 1428081 | 736438 | 691643 | 1352089 | 65007 | 3.36 | 2.78 | 82927 | 640876 | 234140 | 5610338 | 3595450 | 3554094 | 3554094 | 1837649 | 1716445 | 13994294 | 18710254 | 13323060 | 7102474 | 23868426 | 1012163 | 958641 | 11007087 | 1023650 | 10392959 | 1088542 | 1028122 | 990111 | 912087 | 908014 | 888197 | 906409 | 1016393 | 815633 | 699159 | 508500 | 349890 | 230272 | 255883 | 14596063 | 796406 | 820030 | 28954616 | 5795902 | 7332348 | 603396 | 1902087 | 11500677 | 28351220 | 1099136 | 601595 | 972153 | 998707 | 1339610 | 1837748 | 1220161 | 10211181 | 108000 | 21467 | 126710 | 446880 | 3229534 | 4993 | 11500677 | 7563093 | 3900291 | 5712914 | 5787763 | 200692 | 261231.7115 | 10619025 | 9978508 | 1049926 | 1010959 | 1068334 | 1146033 | 1057803 | 1000356 | 900336 | 893942 | 864949 | 855845 | 1054854 | 749300 | 623922 | 440199 | 284583 | 169339 | 142828 | 14358553 | 22771838 | 11608584 | 34.8 | 48504 | 35.8 | 49.5 | 57286 | 33.8 | 89454 | 173734 | 7624216 | 16954978 | 459123 | 454026 | 7616564 | 92692 | 1406614 | 3427384 | 466269 | 37515 | 23827 | 11999638 | 1572787 | 17453939 | 3267049 | 1771782 | 1571022 | 1696027 | 3267049 | 6286745 | 28820 | 28820 | 29707 | 14439 | 14381 | 2062089 | 1969600 | 21626112 | 2091984 | 20371467 | 2234575 | 2085925 | 1990467 | 1812423 | 1801956 | 1753146 | 1762254 | 2071247 | 1564933 | 1323081 | 948699 | 634473 | 399611 | 398711 | 110.8 | 913149 | 28954616 | 3924542 | 4037002 | 1554142 | 257841 | 546026 | 1102103 | 504579 | 298473 | 141680 | 25966 | 3941 | 232406 | 435495 | 27305 | 751696 | 718565 | 539687 | 401342 | 297639 | 111651 | 342816 | 282798 | 522498 | 225754 | 193715 | 190129 | 160088 | 240532 | 1662778 | 199271 | 827432 | 1492651 | 7383 | 1369349 | 27222 | 182557 | 574840 | 1656 | 145357 | 286509 | 195161 | 82795 | 95111 | 17082 | 114932 | 97451 | 70012 | 854973 | 411283 | 295295 | 344420 | 112278 | 4120 | 700747 | 410891 | 133118 | 94734 | 161445 | 243184 | 227922 | 183344 | 38365 | 84691 | 621868 | 44070 | 24312 | 72359 | 201481 | 199469 | 60997 | 21626112 | 124524 | 236631 | 510390 | 229203 | 148579 | 78268 | 11804 | 1715 | 123382 | 270425 | 15874 | 334179 | 322524 | 256069 | 194257 | 156003 | 54214 | 166876 | 141026 | 242077 | 106842 | 94754 | 92548 | 81196 | 118859 | 746527 | 76855 | 359005 | 603599 | 3263 | 582375 | 10984 | 106059 | 311328 | 831 | 73537 | 158365 | 91562 | 43855 | 55012 | 10078 | 56954 | 56138 | 36794 | 392512 | 198958 | 135159 | 153282 | 47834 | 2198 | 438819 | 218771 | 73760 | 51764 | 73393 | 116283 | 113907 | 88717 | 18279 | 32041 | 289865 | 21319 | 12423 | 18523 | 95654 | 64280 | 157 | 10211287 | 1249327 | 785312 | 392300 | 373927 | 178457 | 215169 | 344348 | 912772 | 712376 | 736276 | 2371624 | 752666 | 258831 | 670984 | 2543938 | 1519018 | 3382976 | 702334 | 1391880 | 10211287 | 11236543 | 28954616 | 27 | 4.8 | 671234 | 1025256 | 561071 | 985102 | 66551 | 15750 | 527935 | 20493 | 1095680 | 94609 | 6285595 | 19562731 | 19562731 | 9877750 | 9684981 | 1165365 |
| 2 | 5005.0 | 1774868 | 494056143933 | 721252426439 | 733561461654 | 5780147040071 | 57206 | 55978 | 113184 | 48359.759399 | 1774868 | 920657 | 854211 | 1234468 | 65662 | 3.27 | 2.59 | 95857 | 768205 | 451725 | 4524627 | 2844992 | 3202872 | 3202872 | 1720637 | 1482235 | 10297331 | 13980509 | 9750809 | 4711535 | 15422531 | 541010 | 622797 | 8316966 | 692667 | 7896904 | 736087 | 711718 | 665428 | 617325 | 653985 | 691792 | 725143 | 555213 | 671105 | 583941 | 447188 | 321376 | 227864 | 297383 | 10347030 | 556396 | 439969 | 20070143 | 4071725 | 4262278 | 575494 | 2207562 | 3920105 | 19494649 | 910026 | 562411 | 659740 | 628211 | 822195 | 1168671 | 908502 | 7524226 | 58228 | 16280 | 331729 | 299313 | 1562083 | 3773 | 3920105 | 1648699 | 3075758 | 1994498 | 1925607 | 113184 | 47126.3986 | 7603630 | 7176760 | 563187 | 645491 | 694720 | 735814 | 699391 | 649639 | 591431 | 624744 | 651555 | 664052 | 578913 | 595536 | 496269 | 361798 | 245015 | 158479 | 156451 | 9723113 | 16636184 | 7732110 | 39.0 | 51058 | 40.5 | 53.2 | 63751 | 37.6 | 87167 | 322649 | 5071787 | 9104863 | 369440 | 340441 | 6469341 | 54956 | 1758588 | 2871143 | 410568 | 83316 | 6187 | 10965280 | 866788 | 16150038 | 1645399 | 1699757 | 825459 | 819940 | 1645399 | 3928983 | 9960 | 9960 | 36550 | 5029 | 4931 | 1104197 | 1268288 | 15920596 | 1387387 | 15073664 | 1471901 | 1411109 | 1315067 | 1208756 | 1278729 | 1343347 | 1389195 | 1134126 | 1266641 | 1080210 | 808986 | 566391 | 386343 | 453834 | 425.9 | 709881 | 20070143 | 3595416 | 2182364 | 1012181 | 512090 | 105026 | 4623 | 566085 | 222285 | 250800 | 1375859 | 372083 | 36331 | 199329 | 565803 | 94595 | 113012 | 39543 | 373383 | 232188 | 170661 | 324706 | 344500 | 271067 | 329966 | 269307 | 19946 | 113275 | 239738 | 665 | 189396 | 20342 | 1016 | 1005 | 2888 | 696353 | 107184 | 38266 | 1333128 | 73019 | 173289 | 194976 | 25993 | 345965 | 23485 | 24115 | 145463 | 144351 | 204688 | 104636 | 1156 | 28446 | 11960 | 1150953 | 23619 | 17471 | 149676 | 107735 | 83723 | 278978 | 126444 | 50178 | 647696 | 52872 | 63074 | 73315 | 1056586 | 17171 | 106449 | 111054 | 39680 | 15920596 | 227930 | 45156 | 1971 | 245731 | 101807 | 129891 | 578041 | 153655 | 19294 | 114087 | 297684 | 42464 | 49670 | 19702 | 178348 | 115820 | 85493 | 169528 | 177703 | 127239 | 155409 | 132719 | 9562 | 57642 | 120941 | 284 | 63030 | 8326 | 352 | 535 | 1156 | 332041 | 60446 | 20774 | 569263 | 37915 | 95602 | 102941 | 13893 | 191240 | 11264 | 12914 | 83122 | 86221 | 98353 | 50421 | 584 | 14648 | 1916 | 521013 | 13514 | 9730 | 84095 | 58870 | 42581 | 150061 | 70282 | 28208 | 261620 | 17305 | 24745 | 34054 | 494486 | 4732 | 41867 | 10397 | 263 | 7524399 | 622595 | 687222 | 291132 | 832210 | 56996 | 880881 | 853619 | 111836 | 726892 | 603512 | 73683 | 1218982 | 398654 | 2497180 | 1108306 | 940871 | 2104912 | 239022 | 633845 | 7524399 | 8391638 | 20070143 | 7 | 5.3 | 546522 | 867239 | 152062 | 379625 | 179685 | 56246 | 246121 | 99717 | 374729 | 263656 | 3926227 | 12613979 | 12613979 | 6448602 | 6165377 | 977829 |
| 3 | 4133.0 | 585936 | 487005072197 | 613723856820 | 624450149363 | 6548612367402 | 40665 | 42463 | 83128 | 56491.338012 | 585936 | 316402 | 269534 | 1468305 | 59737 | 3.03 | 2.51 | 75281 | 803267 | 282862 | 5125816 | 2763015 | 3423250 | 3423250 | 1775199 | 1648051 | 9939670 | 14984858 | 9428446 | 5273287 | 15994410 | 540535 | 575794 | 8672285 | 647413 | 8288558 | 693040 | 652863 | 622949 | 602161 | 654907 | 696088 | 756439 | 556332 | 740359 | 711079 | 574407 | 426317 | 297361 | 348670 | 10667264 | 630616 | 434241 | 20875686 | 4066365 | 4142104 | 433652 | 1591194 | 5399203 | 20442034 | 951439 | 366646 | 868205 | 873200 | 1196903 | 1521148 | 970483 | 8152474 | 30738 | 13113 | 201778 | 241103 | 848135 | 3477 | 5399203 | 4060859 | 3663685 | 2732325 | 2666878 | 83128 | 53624.7587 | 8132434 | 7737236 | 561572 | 604324 | 667070 | 728403 | 671610 | 629910 | 591323 | 640640 | 666976 | 693978 | 574960 | 651105 | 617390 | 497457 | 358273 | 236773 | 224380 | 10208422 | 17479459 | 8404539 | 42.3 | 43866 | 43.9 | 55.4 | 52098 | 40.6 | 98640 | 212954 | 4819710 | 9694483 | 320999 | 306853 | 5579880 | 52390 | 572823 | 3221472 | 386749 | 50104 | 11742 | 11181203 | 770061 | 15476483 | 898239 | 2287450 | 433750 | 464489 | 898239 | 5193134 | 15219 | 15219 | 29913 | 7485 | 7734 | 1102107 | 1180118 | 16804719 | 1314483 | 16025794 | 1421443 | 1324473 | 1252859 | 1193484 | 1295547 | 1363064 | 1450417 | 1131292 | 1391464 | 1328469 | 1071864 | 784590 | 534134 | 573050 | 389.3 | 627852 | 20875686 | 2959407 | 2990780 | 1107202 | 139564 | 203442 | 252298 | 342663 | 283344 | 129690 | 129485 | 3407 | 78849 | 131796 | 30196 | 479436 | 544452 | 543860 | 408053 | 288055 | 142999 | 417240 | 573770 | 261579 | 85398 | 333616 | 2029 | 115622 | 143470 | 540715 | 227391 | 933973 | 104898 | 70517 | 460679 | 56767 | 240719 | 452235 | 0 | 207742 | 413860 | 143688 | 644510 | 279740 | 779970 | 766863 | 377196 | 100898 | 428649 | 183680 | 8535 | 297341 | 166979 | 48101 | 323008 | 451913 | 233004 | 178840 | 285711 | 217009 | 234897 | 250408 | 90659 | 40210 | 127174 | 79439 | 13767 | 21497 | 103498 | 109161 | 20707 | 16804719 | 64080 | 84617 | 112750 | 150048 | 139641 | 68081 | 53419 | 1152 | 40455 | 79640 | 18059 | 206336 | 237762 | 248795 | 195362 | 149440 | 70108 | 204783 | 289645 | 118846 | 39620 | 162626 | 1011 | 60130 | 70532 | 238120 | 80416 | 393622 | 42245 | 27259 | 185836 | 27665 | 135609 | 234795 | 0 | 103443 | 231898 | 69296 | 347422 | 155361 | 465971 | 402057 | 222582 | 59214 | 190835 | 81384 | 3488 | 138256 | 62390 | 20779 | 187750 | 229070 | 131273 | 90284 | 117945 | 108972 | 124281 | 117164 | 41705 | 15882 | 54240 | 35494 | 7223 | 4930 | 49689 | 35423 | 57 | 8152541 | 551136 | 659156 | 468362 | 154544 | 90042 | 163107 | 111977 | 692893 | 909338 | 452765 | 967498 | 802706 | 1652607 | 229713 | 1692280 | 1574873 | 2924108 | 1011180 | 720330 | 8152541 | 9790195 | 20875686 | 42 | 5.1 | 511224 | 1637654 | 351168 | 819037 | 78889 | 20495 | 557404 | 36949 | 600047 | 111697 | 5192714 | 15242062 | 15242062 | 7772764 | 7469298 | 1199573 |
| 4 | 3210.0 | 462360 | 315983343213 | 411902114464 | 418518490501 | 4125980999869 | 16036 | 16102 | 32138 | 45285.896438 | 462360 | 240313 | 222047 | 749637 | 61749 | 3.02 | 2.46 | 80493 | 806294 | 236846 | 3167219 | 1763503 | 1470953 | 1470953 | 765013 | 705940 | 6619516 | 9125523 | 6217293 | 3282447 | 9921694 | 335276 | 404682 | 5362136 | 433612 | 5076349 | 410643 | 397480 | 389122 | 375532 | 411895 | 447624 | 487329 | 355242 | 461925 | 408531 | 312098 | 231110 | 173218 | 235227 | 6642616 | 345422 | 268314 | 12992598 | 2573675 | 2724068 | 426311 | 1120165 | 987581 | 12566287 | 549498 | 299343 | 505620 | 485138 | 661013 | 933563 | 649666 | 5117219 | 13841 | 4066 | 72801 | 81708 | 393883 | 1223 | 987581 | 420059 | 2817515 | 484730 | 502851 | 32138 | 44742.7032 | 5010352 | 4726214 | 349790 | 417152 | 441081 | 428491 | 405440 | 392100 | 371081 | 405311 | 435283 | 463856 | 369830 | 429073 | 366389 | 265594 | 180573 | 120018 | 120580 | 6349982 | 10822050 | 5289824 | 41.4 | 46696 | 43.0 | 54.9 | 57362 | 39.8 | 126527 | 185452 | 2988934 | 3115252 | 161589 | 152711 | 3726095 | 18297 | 458294 | 1398152 | 232592 | 16338 | 3998 | 9877346 | 286064 | 12005017 | 410221 | 1270388 | 195431 | 214790 | 410221 | 3463374 | 5221 | 5221 | 32212 | 2622 | 2599 | 685066 | 821834 | 10372488 | 874693 | 9802563 | 839134 | 802920 | 781222 | 746613 | 817206 | 882907 | 951185 | 725072 | 890998 | 774920 | 577692 | 411683 | 293236 | 355807 | 290.4 | 314300 | 12992598 | 1653953 | 1457813 | 585404 | 162479 | 224406 | 22707 | 432943 | 210046 | 93639 | 202612 | 1754 | 84748 | 134652 | 24943 | 212966 | 62172 | 134424 | 548302 | 271213 | 517207 | 239410 | 553292 | 575890 | 903871 | 107575 | 38382 | 88283 | 504795 | 7672 | 14931 | 45556 | 6099 | 2557 | 6112 | 108325 | 136072 | 71952 | 14130 | 273695 | 196055 | 165969 | 5495 | 221381 | 17439 | 19227 | 190704 | 111879 | 144975 | 257849 | 36862 | 11142 | 18502 | 63122 | 23301 | 34327 | 146156 | 125056 | 113432 | 220217 | 317061 | 254390 | 17469 | 0 | 2886 | 271578 | 15924 | 0 | 143081 | 121146 | 29470 | 10372488 | 74277 | 98686 | 10729 | 196059 | 101319 | 45957 | 93730 | 645 | 45424 | 82738 | 12942 | 99476 | 28229 | 66028 | 267265 | 135979 | 258794 | 127103 | 278212 | 269579 | 435594 | 52290 | 18313 | 44342 | 263868 | 3585 | 6189 | 20132 | 2629 | 902 | 2884 | 53850 | 73109 | 39839 | 6765 | 137745 | 110110 | 84780 | 2896 | 116222 | 10022 | 10151 | 101801 | 66453 | 70968 | 124551 | 18223 | 5868 | 6096 | 31133 | 14285 | 17713 | 80722 | 66212 | 56799 | 116780 | 175679 | 129971 | 7816 | 0 | 1255 | 123001 | 7629 | 0 | 57457 | 25114 | 82 | 5117327 | 481070 | 210065 | 479229 | 139701 | 82571 | 185756 | 118011 | 193733 | 1067353 | 1083986 | 36321 | 506198 | 307545 | 287794 | 465570 | 1070547 | 1730882 | 522496 | 1039956 | 5117327 | 5703807 | 12992598 | 22 | 6.1 | 402223 | 586480 | 222750 | 599167 | 27840 | 4755 | 326829 | 6624 | 516090 | 45734 | 3463173 | 10297405 | 10297405 | 5261612 | 5035793 | 725053 |
[(test_newstate_df.columns.get_loc(c),c) for c in test_newstate_df]
[(0, 'Provider_Count'), (1, 'AAGEBASECY'), (2, 'AGGDI_CY'), (3, 'AGGHINC_CY'), (4, 'AGGINC_CY'), (5, 'AGGNW_CY'), (6, 'AIFBASE_CY'), (7, 'AIMBASE_CY'), (8, 'AMERIND_CY'), (9, 'AREA'), (10, 'ASIAN_CY'), (11, 'ASNFBASECY'), (12, 'ASNMBASECY'), (13, 'ASSCDEG_CY'), (14, 'AVGDI_CY'), (15, 'AVGFMSZ_CY'), (16, 'AVGHHSZ_CY'), (17, 'AVGHINC_CY'), (18, 'AVGNW_CY'), (19, 'AVGVAL_CY'), (20, 'BABYBOOMCY'), (21, 'BACHDEG_CY'), (22, 'BAGEBASECY'), (23, 'BLACK_CY'), (24, 'BLKFBASECY'), (25, 'BLKMBASECY'), (26, 'CIVLBFR_CY'), (27, 'EDUCBASECY'), (28, 'EMP_CY'), (29, 'FAMHH_CY'), (30, 'FAMPOP_CY'), (31, 'FEM0_CY'), (32, 'FEM15_CY'), (33, 'FEM18UP_CY'), (34, 'FEM20_CY'), (35, 'FEM21UP_CY'), (36, 'FEM25_CY'), (37, 'FEM30_CY'), (38, 'FEM35_CY'), (39, 'FEM40_CY'), (40, 'FEM45_CY'), (41, 'FEM50_CY'), (42, 'FEM55_CY'), (43, 'FEM5_CY'), (44, 'FEM60_CY'), (45, 'FEM65_CY'), (46, 'FEM70_CY'), (47, 'FEM75_CY'), (48, 'FEM80_CY'), (49, 'FEM85_CY'), (50, 'FEMALES_CY'), (51, 'GED_CY'), (52, 'GENALPHACY'), (53, 'GENBASE_CY'), (54, 'GENX_CY'), (55, 'GENZ_CY'), (56, 'GQPOP_CY'), (57, 'GRADDEG_CY'), (58, 'HAGEBASECY'), (59, 'HHPOP_CY'), (60, 'HINC0_CY'), (61, 'HINC150_CY'), (62, 'HINC15_CY'), (63, 'HINC25_CY'), (64, 'HINC35_CY'), (65, 'HINC50_CY'), (66, 'HINC75_CY'), (67, 'HINCBASECY'), (68, 'HISPAI_CY'), (69, 'HISPASN_CY'), (70, 'HISPBLK_CY'), (71, 'HISPMLT_CY'), (72, 'HISPOTH_CY'), (73, 'HISPPI_CY'), (74, 'HISPPOP_CY'), (75, 'HISPWHT_CY'), (76, 'HSGRAD_CY'), (77, 'HSPFBASECY'), (78, 'HSPMBASECY'), (79, 'IAGEBASECY'), (80, 'LANDAREA'), (81, 'MAL18UP_CY'), (82, 'MAL21UP_CY'), (83, 'MALE0_CY'), (84, 'MALE15_CY'), (85, 'MALE20_CY'), (86, 'MALE25_CY'), (87, 'MALE30_CY'), (88, 'MALE35_CY'), (89, 'MALE40_CY'), (90, 'MALE45_CY'), (91, 'MALE50_CY'), (92, 'MALE55_CY'), (93, 'MALE5_CY'), (94, 'MALE60_CY'), (95, 'MALE65_CY'), (96, 'MALE70_CY'), (97, 'MALE75_CY'), (98, 'MALE80_CY'), (99, 'MALE85_CY'), (100, 'MALES_CY'), (101, 'MARBASE_CY'), (102, 'MARRIED_CY'), (103, 'MEDAGE_CY'), (104, 'MEDDI_CY'), (105, 'MEDFAGE_CY'), (106, 'MEDHHR_CY'), (107, 'MEDHINC_CY'), (108, 'MEDMAGE_CY'), (109, 'MEDNW_CY'), (110, 'MEDVAL_CY'), (111, 'MILLENN_CY'), (112, 'MINORITYCY'), (113, 'MLTFBASECY'), (114, 'MLTMBASECY'), (115, 'NEVMARR_CY'), (116, 'NHSPAI_CY'), (117, 'NHSPASN_CY'), (118, 'NHSPBLK_CY'), (119, 'NHSPMLT_CY'), (120, 'NHSPOTH_CY'), (121, 'NHSPPI_CY'), (122, 'NHSPWHT_CY'), (123, 'NOHS_CY'), (124, 'NONHISP_CY'), (125, 'OAGEBASECY'), (126, 'OLDRGENSCY'), (127, 'OTHFBASECY'), (128, 'OTHMBASECY'), (129, 'OTHRACE_CY'), (130, 'OWNER_CY'), (131, 'PACIFIC_CY'), (132, 'PAGEBASECY'), (133, 'PCI_CY'), (134, 'PIFBASE_CY'), (135, 'PIMBASE_CY'), (136, 'POP0_CY'), (137, 'POP15_CY'), (138, 'POP18UP_CY'), (139, 'POP20_CY'), (140, 'POP21UP_CY'), (141, 'POP25_CY'), (142, 'POP30_CY'), (143, 'POP35_CY'), (144, 'POP40_CY'), (145, 'POP45_CY'), (146, 'POP50_CY'), (147, 'POP55_CY'), (148, 'POP5_CY'), (149, 'POP60_CY'), (150, 'POP65_CY'), (151, 'POP70_CY'), (152, 'POP75_CY'), (153, 'POP80_CY'), (154, 'POP85_CY'), (155, 'POPDENS_CY'), (156, 'RACE2UP_CY'), (157, 'RACEBASECY'), (158, 'RENTER_CY'), (159, 'SMCOLL_CY'), (160, 'SOMEHS_CY'), (161, 'TADULT01'), (162, 'TADULT02'), (163, 'TADULT03'), (164, 'TADULT04'), (165, 'TADULT05'), (166, 'TADULT06'), (167, 'TADULT07'), (168, 'TADULT08'), (169, 'TADULT09'), (170, 'TADULT11'), (171, 'TADULT12'), (172, 'TADULT13'), (173, 'TADULT14'), (174, 'TADULT15'), (175, 'TADULT16'), (176, 'TADULT17'), (177, 'TADULT18'), (178, 'TADULT19'), (179, 'TADULT20'), (180, 'TADULT21'), (181, 'TADULT22'), (182, 'TADULT23'), (183, 'TADULT24'), (184, 'TADULT25'), (185, 'TADULT26'), (186, 'TADULT27'), (187, 'TADULT28'), (188, 'TADULT29'), (189, 'TADULT30'), (190, 'TADULT31'), (191, 'TADULT32'), (192, 'TADULT33'), (193, 'TADULT34'), (194, 'TADULT35'), (195, 'TADULT36'), (196, 'TADULT37'), (197, 'TADULT38'), (198, 'TADULT39'), (199, 'TADULT40'), (200, 'TADULT41'), (201, 'TADULT42'), (202, 'TADULT43'), (203, 'TADULT44'), (204, 'TADULT45'), (205, 'TADULT46'), (206, 'TADULT47'), (207, 'TADULT48'), (208, 'TADULT49'), (209, 'TADULT50'), (210, 'TADULT51'), (211, 'TADULT52'), (212, 'TADULT53'), (213, 'TADULT54'), (214, 'TADULT55'), (215, 'TADULT56'), (216, 'TADULT57'), (217, 'TADULT58'), (218, 'TADULT59'), (219, 'TADULT60'), (220, 'TADULT61'), (221, 'TADULT62'), (222, 'TADULT63'), (223, 'TADULT64'), (224, 'TADULT65'), (225, 'TADULT66'), (226, 'TADULT67'), (227, 'TADULT68'), (228, 'TADULTBASE'), (229, 'THH01'), (230, 'THH02'), (231, 'THH03'), (232, 'THH04'), (233, 'THH05'), (234, 'THH06'), (235, 'THH07'), (236, 'THH08'), (237, 'THH09'), (238, 'THH11'), (239, 'THH12'), (240, 'THH13'), (241, 'THH14'), (242, 'THH15'), (243, 'THH16'), (244, 'THH17'), (245, 'THH18'), (246, 'THH19'), (247, 'THH20'), (248, 'THH21'), (249, 'THH22'), (250, 'THH23'), (251, 'THH24'), (252, 'THH25'), (253, 'THH26'), (254, 'THH27'), (255, 'THH28'), (256, 'THH29'), (257, 'THH30'), (258, 'THH31'), (259, 'THH32'), (260, 'THH33'), (261, 'THH34'), (262, 'THH35'), (263, 'THH36'), (264, 'THH37'), (265, 'THH38'), (266, 'THH39'), (267, 'THH40'), (268, 'THH41'), (269, 'THH42'), (270, 'THH43'), (271, 'THH44'), (272, 'THH45'), (273, 'THH46'), (274, 'THH47'), (275, 'THH48'), (276, 'THH49'), (277, 'THH50'), (278, 'THH51'), (279, 'THH52'), (280, 'THH53'), (281, 'THH54'), (282, 'THH55'), (283, 'THH56'), (284, 'THH57'), (285, 'THH58'), (286, 'THH59'), (287, 'THH60'), (288, 'THH61'), (289, 'THH62'), (290, 'THH63'), (291, 'THH64'), (292, 'THH65'), (293, 'THH66'), (294, 'THH67'), (295, 'THH68'), (296, 'THHBASE'), (297, 'THHGRPL1'), (298, 'THHGRPL11'), (299, 'THHGRPL12'), (300, 'THHGRPL13'), (301, 'THHGRPL14'), (302, 'THHGRPL2'), (303, 'THHGRPL3'), (304, 'THHGRPL4'), (305, 'THHGRPL5'), (306, 'THHGRPL6'), (307, 'THHGRPL7'), (308, 'THHGRPL8'), (309, 'THHGRPL9'), (310, 'THHGRPU1'), (311, 'THHGRPU2'), (312, 'THHGRPU3'), (313, 'THHGRPU4'), (314, 'THHGRPU5'), (315, 'THHGRPU6'), (316, 'TOTHH_CY'), (317, 'TOTHU_CY'), (318, 'TOTPOP_CY'), (319, 'TSEGNUM'), (320, 'UNEMPRT_CY'), (321, 'UNEMP_CY'), (322, 'VACANT_CY'), (323, 'VAL0_CY'), (324, 'VAL150K_CY'), (325, 'VAL1M_CY'), (326, 'VAL1PT5MCY'), (327, 'VAL250K_CY'), (328, 'VAL2M_CY'), (329, 'VAL50K_CY'), (330, 'VAL750K_CY'), (331, 'VALBASE_CY'), (332, 'WAGEBASECY'), (333, 'WHITE_CY'), (334, 'WHTFBASECY'), (335, 'WHTMBASECY'), (336, 'WIDOWED_CY')]
# Standard Scalar
from sklearn.preprocessing import StandardScaler
sc_state = StandardScaler()
newstate_df_std = sc_state.fit_transform(test_newstate_df)
from sklearn.preprocessing import PowerTransformer
pt_state = PowerTransformer(method='yeo-johnson')
newstate_df_pt = pt_state.fit_transform(test_newstate_df)
# Import and fit PCA
from sklearn.decomposition import PCA
# pca_statetest = PCA().fit(newstate_df_std)
# pca_newstate = pca_statetest.transform(newstate_df_std)
pca_statetest = PCA(n_components=8)
pca_newstate = pca_statetest.fit_transform(newstate_df_std)
pca_statetest.components_
array([[ 0.067079 , 0.05809787, 0.06796729, ..., 0.06698423,
0.06715825, 0.0671426 ],
[-0.00357032, 0.07835758, 0.00763204, ..., -0.02473751,
-0.02266122, -0.01930669],
[ 0.03209029, 0.00308058, 0.00831319, ..., 0.01021552,
0.00718204, 0.0188534 ],
...,
[ 0.02761109, -0.02721144, 0.00358023, ..., -0.00915768,
-0.01188302, 0.01111088],
[-0.00190508, -0.02394761, -0.00424891, ..., 0.01495248,
0.01407101, -0.00246226],
[-0.00147688, 0.0013432 , -0.01253322, ..., -0.0157983 ,
-0.01461408, -0.0041321 ]])
pca_statetest.explained_variance_ratio_
array([0.63796413, 0.11235257, 0.05632679, 0.03036847, 0.02757483,
0.02411164, 0.01938572, 0.01406097])
# Show percentage of variance explained by different principal components
sns.set(rc={'figure.figsize':(7,7)})
labels = ['PC' + str(x) for x in range(1,9)]
plt.plot(np.cumsum(pca_statetest.explained_variance_ratio_))
plt.bar(x=range(0,8), height=pca_statetest.explained_variance_ratio_, tick_label = labels)
plt.xlabel('Number of components')
# plt.yticks(range(0,1,0.1))
# plt.ylim(bottom=0.90)
plt.ylabel('Cumulative explained variance');
The variance explained by PC1 is ~64% and the cumulative variance explained by first 5 components is ~86.5%.
# Create df for PCs with State Labels
pca_df = pd.DataFrame(data=pca_newstate, index=newstate_obgyn_df.RegionAbbr, columns = ['PC1','PC2','PC3','PC4','PC5','PC6','PC7','PC8'])
pca_df
| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 | |
|---|---|---|---|---|---|---|---|---|
| RegionAbbr | ||||||||
| CA | 67.320404 | 25.177160 | -3.101485 | -4.706037 | 4.407026 | -5.775390 | -2.811014 | -0.146043 |
| TX | 43.191556 | -10.699602 | -15.687442 | -0.315688 | -10.453568 | 3.322438 | 3.345736 | -4.351121 |
| NY | 26.419407 | 3.238080 | 15.933188 | -5.934788 | 1.347212 | 10.766314 | 8.520713 | 0.926305 |
| FL | 31.432822 | -12.900757 | -0.393267 | 14.717084 | 10.657921 | 0.852195 | 2.207121 | 1.376193 |
| PA | 13.531901 | -8.907294 | 10.182844 | 0.293992 | -2.072087 | -4.840718 | -2.510874 | -1.370546 |
| IL | 12.417125 | -3.635323 | 4.527603 | -0.628122 | -3.899572 | -0.172909 | -2.054802 | 0.271814 |
| MI | 7.217125 | -10.407115 | 5.212897 | -2.265537 | -0.960765 | -4.309118 | 0.029509 | 2.223657 |
| OH | 10.338226 | -12.537128 | 7.279680 | -2.746292 | -1.933483 | -5.282655 | -0.098395 | 2.661954 |
| NJ | 4.728497 | 7.498301 | 5.688313 | 3.692673 | -2.532585 | 3.866985 | -2.258937 | -2.237845 |
| GA | 7.547692 | -6.710732 | -4.032778 | -1.570728 | -0.257958 | 5.436692 | -5.449393 | 2.504245 |
| NC | 8.038400 | -7.848564 | -3.433742 | -1.721384 | 2.439704 | 2.483189 | -3.253880 | 1.830589 |
| VA | 4.503082 | -0.000169 | 0.672323 | 1.913937 | -3.322481 | 1.700228 | -3.637186 | -0.418838 |
| MA | 1.282166 | 6.055139 | 5.967845 | 4.164720 | -2.779457 | 1.181995 | -1.456220 | -2.180047 |
| MD | -0.441701 | 3.928366 | 2.364572 | 4.031252 | -3.604813 | 2.952363 | -3.518543 | -0.148252 |
| MO | -0.698659 | -4.567963 | 0.932780 | -1.230552 | -0.061544 | -1.911370 | -0.223808 | 0.420068 |
| WA | 3.281982 | 3.186541 | -0.230398 | 3.429841 | -1.409232 | -2.330611 | -0.260901 | 2.168421 |
| AZ | 2.376126 | 0.789323 | -5.752435 | 2.984429 | 2.584803 | -0.532572 | 5.620356 | 1.256825 |
| IN | -0.342637 | -5.899252 | 2.172777 | -2.984052 | -1.289292 | -3.436518 | 0.616144 | 1.555948 |
| TN | 0.079788 | -5.418673 | -0.295000 | -2.671651 | 1.977797 | 0.962691 | -2.925767 | -0.271666 |
| CO | -0.772431 | 2.227808 | -1.598518 | 2.964669 | -3.123626 | -0.621338 | 0.963963 | 0.414342 |
| SC | -3.111514 | -3.274919 | -1.991848 | -1.300529 | 3.252373 | 2.473097 | -2.832687 | 0.298146 |
| CT | -5.413854 | 4.798944 | 3.710933 | 4.236997 | -2.277963 | 0.544973 | -1.859746 | -2.321968 |
| LA | -3.581901 | -3.539782 | -2.332202 | -2.804084 | 2.367840 | 2.844284 | -2.603643 | 0.000065 |
| MN | -1.454759 | -0.622348 | 0.768827 | 2.276174 | -3.472325 | -2.768814 | 1.574324 | -0.337149 |
| WI | -1.285486 | -3.390711 | 2.253455 | 0.057863 | -1.703981 | -4.243503 | 1.522183 | -0.247803 |
| KY | -4.590302 | -3.140127 | -1.049884 | -3.308739 | 2.250637 | 0.062161 | -1.280529 | -1.827113 |
| AL | -3.570830 | -4.164563 | -1.423220 | -3.164216 | 2.903233 | 2.307430 | -3.509442 | -0.372152 |
| OR | -3.862786 | 1.427036 | -0.443500 | 2.416649 | 1.343724 | -1.949341 | 0.876820 | -0.158645 |
| OK | -4.495779 | -1.361834 | -3.105018 | -3.951288 | 1.807075 | -1.827505 | 3.818706 | 0.681093 |
| MS | -7.501089 | -2.003411 | -2.217468 | -3.664372 | 3.327493 | 2.845254 | -2.441301 | -0.122287 |
| NV | -6.581356 | 2.652540 | -2.832970 | 0.693487 | 0.786875 | 0.561275 | 1.432138 | 1.215541 |
| UT | -6.831176 | 3.590845 | -3.854658 | 0.185379 | -3.358245 | 0.908831 | 0.786651 | 5.289378 |
| KS | -7.122027 | 0.276723 | -0.867313 | -1.133046 | -1.212334 | -1.468594 | 1.855928 | 0.185332 |
| IA | -7.047168 | -0.692963 | 0.517779 | -1.237969 | -1.368690 | -3.127834 | 2.332365 | -1.073142 |
| AR | -7.352917 | -1.480066 | -1.769965 | -2.931082 | 2.997852 | 0.493442 | -0.701777 | -0.915410 |
| NM | -8.160223 | 1.737526 | -3.413789 | -1.572444 | 2.912151 | -0.121008 | 2.905147 | -0.173036 |
| HI | -8.395282 | 9.076429 | -1.174982 | 1.055486 | -0.345158 | -1.317902 | -2.715543 | 6.841207 |
| NE | -9.341990 | 1.425450 | -0.998756 | -0.901640 | -0.879588 | -1.076031 | 2.153185 | -0.367484 |
| WV | -9.832298 | -0.713482 | -0.727067 | -2.904878 | 3.543541 | -0.540629 | -0.484177 | -4.985301 |
| DC | -11.485659 | 5.451734 | 0.516645 | 1.684653 | -2.798453 | 3.776787 | -0.524282 | 2.941622 |
| RI | -10.795428 | 3.588724 | 0.789578 | 1.185230 | 0.423904 | 0.171334 | -0.456088 | -1.892776 |
| NH | -10.344747 | 3.464786 | 1.295601 | 2.833957 | -0.707908 | -0.410803 | -0.556011 | -2.257321 |
| ID | -9.677239 | 1.792361 | -1.934014 | -1.027773 | 0.192167 | -0.325320 | 1.408168 | 0.853828 |
| DE | -11.094500 | 3.324309 | 0.118532 | 1.415230 | 0.149220 | 0.565963 | -0.544958 | -1.095819 |
| ME | -10.374494 | 1.575919 | 0.810365 | 0.686126 | 2.447631 | -1.300093 | 0.302217 | -3.736477 |
| MT | -10.774798 | 1.862764 | -0.845265 | -0.572628 | 1.772330 | -1.109617 | 1.917061 | -1.859826 |
| AK | -10.880967 | 4.775845 | -3.105081 | 0.538624 | -2.186515 | 1.112020 | 2.604951 | 4.029744 |
| VT | -11.859212 | 2.796808 | 0.569223 | 0.907925 | 1.506416 | -0.579738 | -0.021773 | -3.186915 |
| SD | -11.292890 | 2.405303 | -1.424176 | -0.907701 | 0.628139 | -0.664910 | 1.986350 | -0.698878 |
| WY | -11.878690 | 3.039309 | -1.161336 | -0.089128 | 0.301224 | 0.210282 | 0.711577 | -0.421634 |
| ND | -11.459512 | 2.752705 | -1.088186 | -0.120025 | -0.316664 | -0.357381 | 1.500362 | -0.770826 |
per_var = np.round(pca_statetest.explained_variance_ratio_*100, decimals=1)
plt.scatter(pca_df.PC1, pca_df.PC2)
plt.xlabel('PC1 - {0}%'.format(per_var[0]))
plt.ylabel('PC2 - {0}%'.format(per_var[1]))
for sample in pca_df.index:
plt.annotate(sample, (pca_df.PC1.loc[sample],pca_df.PC2.loc[sample]))
This graph of first 2 components suggest that the cluster of states on the left (DC, UT, AK, CT) are correlated with each other. Similarly states in the bottom center (GA, NC, MI etc) are correlated with each other. The separation of 2 clusters along x-axis suggests that states towards the left are very different from states towards the right.
componentdf = pd.DataFrame(data=pca_statetest.components_, columns = test_newstate_df.columns)
componentdf.head()
| Provider_Count | AAGEBASECY | AGGDI_CY | AGGHINC_CY | AGGINC_CY | AGGNW_CY | AIFBASE_CY | AIMBASE_CY | AMERIND_CY | AREA | ASIAN_CY | ASNFBASECY | ASNMBASECY | ASSCDEG_CY | AVGDI_CY | AVGFMSZ_CY | AVGHHSZ_CY | AVGHINC_CY | AVGNW_CY | AVGVAL_CY | BABYBOOMCY | BACHDEG_CY | BAGEBASECY | BLACK_CY | BLKFBASECY | BLKMBASECY | CIVLBFR_CY | EDUCBASECY | EMP_CY | FAMHH_CY | FAMPOP_CY | FEM0_CY | FEM15_CY | FEM18UP_CY | FEM20_CY | FEM21UP_CY | FEM25_CY | FEM30_CY | FEM35_CY | FEM40_CY | FEM45_CY | FEM50_CY | FEM55_CY | FEM5_CY | FEM60_CY | FEM65_CY | FEM70_CY | FEM75_CY | FEM80_CY | FEM85_CY | FEMALES_CY | GED_CY | GENALPHACY | GENBASE_CY | GENX_CY | GENZ_CY | GQPOP_CY | GRADDEG_CY | HAGEBASECY | HHPOP_CY | HINC0_CY | HINC150_CY | HINC15_CY | HINC25_CY | HINC35_CY | HINC50_CY | HINC75_CY | HINCBASECY | HISPAI_CY | HISPASN_CY | HISPBLK_CY | HISPMLT_CY | HISPOTH_CY | HISPPI_CY | HISPPOP_CY | HISPWHT_CY | HSGRAD_CY | HSPFBASECY | HSPMBASECY | IAGEBASECY | LANDAREA | MAL18UP_CY | MAL21UP_CY | MALE0_CY | MALE15_CY | MALE20_CY | MALE25_CY | MALE30_CY | MALE35_CY | MALE40_CY | MALE45_CY | MALE50_CY | MALE55_CY | MALE5_CY | MALE60_CY | MALE65_CY | MALE70_CY | MALE75_CY | MALE80_CY | MALE85_CY | MALES_CY | MARBASE_CY | MARRIED_CY | MEDAGE_CY | MEDDI_CY | MEDFAGE_CY | MEDHHR_CY | MEDHINC_CY | MEDMAGE_CY | MEDNW_CY | MEDVAL_CY | MILLENN_CY | MINORITYCY | MLTFBASECY | MLTMBASECY | NEVMARR_CY | NHSPAI_CY | NHSPASN_CY | NHSPBLK_CY | NHSPMLT_CY | NHSPOTH_CY | NHSPPI_CY | NHSPWHT_CY | NOHS_CY | NONHISP_CY | OAGEBASECY | OLDRGENSCY | OTHFBASECY | OTHMBASECY | OTHRACE_CY | OWNER_CY | PACIFIC_CY | PAGEBASECY | PCI_CY | PIFBASE_CY | PIMBASE_CY | POP0_CY | POP15_CY | POP18UP_CY | POP20_CY | POP21UP_CY | POP25_CY | POP30_CY | POP35_CY | POP40_CY | POP45_CY | POP50_CY | POP55_CY | POP5_CY | POP60_CY | POP65_CY | POP70_CY | POP75_CY | POP80_CY | POP85_CY | POPDENS_CY | RACE2UP_CY | RACEBASECY | RENTER_CY | SMCOLL_CY | SOMEHS_CY | TADULT01 | TADULT02 | TADULT03 | TADULT04 | TADULT05 | TADULT06 | TADULT07 | TADULT08 | TADULT09 | TADULT11 | TADULT12 | TADULT13 | TADULT14 | TADULT15 | TADULT16 | TADULT17 | TADULT18 | TADULT19 | TADULT20 | TADULT21 | TADULT22 | TADULT23 | TADULT24 | TADULT25 | TADULT26 | TADULT27 | TADULT28 | TADULT29 | TADULT30 | TADULT31 | TADULT32 | TADULT33 | TADULT34 | TADULT35 | TADULT36 | TADULT37 | TADULT38 | TADULT39 | TADULT40 | TADULT41 | TADULT42 | TADULT43 | TADULT44 | TADULT45 | TADULT46 | TADULT47 | TADULT48 | TADULT49 | TADULT50 | TADULT51 | TADULT52 | TADULT53 | TADULT54 | TADULT55 | TADULT56 | TADULT57 | TADULT58 | TADULT59 | TADULT60 | TADULT61 | TADULT62 | TADULT63 | TADULT64 | TADULT65 | TADULT66 | TADULT67 | TADULT68 | TADULTBASE | THH01 | THH02 | THH03 | THH04 | THH05 | THH06 | THH07 | THH08 | THH09 | THH11 | THH12 | THH13 | THH14 | THH15 | THH16 | THH17 | THH18 | THH19 | THH20 | THH21 | THH22 | THH23 | THH24 | THH25 | THH26 | THH27 | THH28 | THH29 | THH30 | THH31 | THH32 | THH33 | THH34 | THH35 | THH36 | THH37 | THH38 | THH39 | THH40 | THH41 | THH42 | THH43 | THH44 | THH45 | THH46 | THH47 | THH48 | THH49 | THH50 | THH51 | THH52 | THH53 | THH54 | THH55 | THH56 | THH57 | THH58 | THH59 | THH60 | THH61 | THH62 | THH63 | THH64 | THH65 | THH66 | THH67 | THH68 | THHBASE | THHGRPL1 | THHGRPL11 | THHGRPL12 | THHGRPL13 | THHGRPL14 | THHGRPL2 | THHGRPL3 | THHGRPL4 | THHGRPL5 | THHGRPL6 | THHGRPL7 | THHGRPL8 | THHGRPL9 | THHGRPU1 | THHGRPU2 | THHGRPU3 | THHGRPU4 | THHGRPU5 | THHGRPU6 | TOTHH_CY | TOTHU_CY | TOTPOP_CY | TSEGNUM | UNEMPRT_CY | UNEMP_CY | VACANT_CY | VAL0_CY | VAL150K_CY | VAL1M_CY | VAL1PT5MCY | VAL250K_CY | VAL2M_CY | VAL50K_CY | VAL750K_CY | VALBASE_CY | WAGEBASECY | WHITE_CY | WHTFBASECY | WHTMBASECY | WIDOWED_CY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.067079 | 0.058098 | 0.067967 | 0.067621 | 0.067624 | 0.067112 | 0.036234 | 0.037716 | 0.036973 | 0.010595 | 0.058098 | 0.057994 | 0.058208 | 0.067235 | 0.014692 | 0.027737 | 0.025862 | 0.014430 | 0.009782 | 0.017051 | 0.067878 | 0.067814 | 0.053549 | 0.053549 | 0.052824 | 0.054324 | 0.068043 | 0.068133 | 0.068035 | 0.068029 | 0.068037 | 0.067392 | 0.067874 | 0.068112 | 0.067946 | 0.068110 | 0.067880 | 0.067885 | 0.067926 | 0.067998 | 0.068053 | 0.068018 | 0.067958 | 0.067525 | 0.067857 | 0.067614 | 0.067222 | 0.066847 | 0.066401 | 0.065686 | 0.068118 | 0.063479 | 0.067358 | 0.068125 | 0.068075 | 0.067689 | 0.066467 | 0.066317 | 0.062484 | 0.068126 | 0.066208 | 0.066029 | 0.066431 | 0.066181 | 0.066665 | 0.067362 | 0.067875 | 0.067948 | 0.059589 | 0.055683 | 0.051291 | 0.062407 | 0.059857 | 0.052641 | 0.062484 | 0.060889 | 0.066250 | 0.062488 | 0.062474 | 0.036973 | 0.010548 | 0.068157 | 0.068160 | 0.067402 | 0.067866 | 0.067924 | 0.067860 | 0.067865 | 0.067921 | 0.068015 | 0.068094 | 0.068070 | 0.067994 | 0.067524 | 0.067902 | 0.067688 | 0.067248 | 0.066776 | 0.066235 | 0.065636 | 0.068121 | 0.068141 | 0.068104 | -0.008828 | 0.009330 | -0.007062 | -0.006297 | 0.006627 | -0.011373 | -0.003487 | 0.012110 | 0.067917 | 0.065704 | 0.064007 | 0.063799 | 0.067857 | 0.017708 | 0.058119 | 0.052785 | 0.063430 | 0.056606 | 0.032225 | 0.064021 | 0.064803 | 0.066425 | 0.060082 | 0.066610 | 0.059936 | 0.060213 | 0.060082 | 0.067147 | 0.034347 | 0.034347 | 0.004348 | 0.034759 | 0.033930 | 0.067397 | 0.067872 | 0.068141 | 0.067940 | 0.068141 | 0.067874 | 0.067880 | 0.067930 | 0.068012 | 0.068078 | 0.068048 | 0.067980 | 0.067525 | 0.067885 | 0.067654 | 0.067242 | 0.066832 | 0.066369 | 0.065761 | -0.005488 | 0.063908 | 0.068125 | 0.067652 | 0.067676 | 0.067310 | 0.055469 | 0.058244 | 0.055469 | 0.055196 | 0.061669 | 0.054852 | 0.045551 | 0.044954 | 0.050047 | 0.059840 | 0.049609 | 0.058228 | 0.058247 | 0.032323 | 0.048176 | 0.058037 | 0.027361 | 0.036779 | 0.043737 | 0.037506 | 0.020139 | 0.050777 | 0.001219 | 0.036922 | 0.023664 | 0.046224 | 0.050045 | 0.056971 | 0.043543 | 0.046956 | 0.042867 | 0.046537 | 0.053831 | 0.052349 | 0.029883 | 0.048035 | 0.057004 | 0.034282 | 0.034117 | 0.055958 | 0.030280 | 0.038379 | 0.051657 | 0.060586 | 0.026127 | 0.022421 | 0.010628 | 0.045219 | 0.008275 | 0.022589 | 0.048960 | 0.056922 | 0.052426 | 0.036720 | 0.040956 | 0.024883 | 0.035746 | 0.026889 | 0.049271 | 0.047622 | 0.056287 | 0.048052 | 0.021827 | 0.049742 | 0.058882 | 0.061360 | 0.060507 | 0.068141 | 0.056054 | 0.057746 | 0.054611 | 0.054877 | 0.062198 | 0.055330 | 0.045132 | 0.045441 | 0.050367 | 0.059038 | 0.049893 | 0.057119 | 0.057731 | 0.031374 | 0.047457 | 0.057408 | 0.026786 | 0.035501 | 0.043122 | 0.036897 | 0.019834 | 0.050372 | 0.000097 | 0.036246 | 0.022835 | 0.044746 | 0.050393 | 0.056702 | 0.042578 | 0.047109 | 0.042227 | 0.046101 | 0.054112 | 0.051746 | 0.029738 | 0.047725 | 0.056214 | 0.033174 | 0.034145 | 0.056224 | 0.030600 | 0.037866 | 0.051180 | 0.059060 | 0.025046 | 0.021073 | 0.009522 | 0.044457 | 0.006334 | 0.022736 | 0.047624 | 0.056655 | 0.051269 | 0.036442 | 0.039861 | 0.024217 | 0.034967 | 0.026457 | 0.049263 | 0.047824 | 0.054559 | 0.047645 | 0.022017 | 0.046895 | 0.058053 | 0.057093 | 0.054201 | 0.067948 | 0.064084 | 0.060069 | 0.034802 | 0.056153 | 0.060691 | 0.052587 | 0.056097 | 0.053469 | 0.048462 | 0.036268 | 0.057089 | 0.064164 | 0.047533 | 0.050115 | 0.065278 | 0.058679 | 0.066223 | 0.050364 | 0.037789 | 0.067948 | 0.067751 | 0.068125 | -0.002565 | 0.013824 | 0.067448 | 0.060060 | 0.048786 | 0.050060 | 0.053909 | 0.052876 | 0.061415 | 0.053834 | 0.043232 | 0.054522 | 0.067146 | 0.067075 | 0.067075 | 0.066984 | 0.067158 | 0.067143 |
| 1 | -0.003570 | 0.078358 | 0.007632 | 0.013815 | 0.013850 | 0.007650 | 0.032811 | 0.033594 | 0.033203 | 0.011870 | 0.078358 | 0.078733 | 0.077934 | -0.011698 | 0.086543 | 0.058789 | 0.047331 | 0.087002 | 0.062984 | 0.107941 | -0.009610 | 0.009897 | -0.059093 | -0.059093 | -0.059540 | -0.058562 | -0.000099 | -0.002924 | 0.000294 | -0.009092 | -0.000135 | 0.001447 | 0.000411 | -0.003220 | 0.001643 | -0.003320 | 0.006778 | 0.006489 | 0.002683 | -0.000206 | -0.001863 | -0.003488 | -0.006710 | -0.000264 | -0.009542 | -0.013022 | -0.014909 | -0.015643 | -0.015773 | -0.011326 | -0.002369 | -0.050427 | 0.001937 | -0.001592 | -0.000534 | 0.000631 | -0.010569 | 0.016594 | 0.038929 | -0.001388 | -0.031004 | 0.031528 | -0.034124 | -0.037422 | -0.033052 | -0.024669 | -0.010650 | -0.011740 | 0.061031 | 0.081668 | 0.002465 | 0.058781 | 0.063432 | 0.075984 | 0.038929 | 0.017741 | -0.028801 | 0.038922 | 0.038932 | 0.033203 | 0.012102 | -0.001403 | -0.001544 | 0.001788 | 0.001720 | 0.003512 | 0.007357 | 0.007864 | 0.004619 | 0.001246 | -0.000635 | -0.002192 | -0.005564 | 0.000285 | -0.008973 | -0.013372 | -0.015794 | -0.015676 | -0.013866 | -0.005600 | -0.000795 | -0.002121 | -0.003418 | -0.023117 | 0.091761 | -0.026162 | -0.017788 | 0.090619 | -0.019082 | 0.044550 | 0.108742 | 0.005450 | 0.028642 | 0.048676 | 0.049491 | 0.008129 | 0.012079 | 0.078280 | -0.061888 | 0.039897 | 0.034381 | 0.086034 | -0.040299 | 0.035399 | -0.023884 | 0.063222 | -0.013902 | 0.064405 | 0.062076 | 0.063222 | -0.026175 | 0.085895 | 0.085895 | 0.068772 | 0.086375 | 0.085400 | 0.001621 | 0.001083 | -0.002333 | 0.002598 | -0.002455 | 0.007075 | 0.007187 | 0.003660 | 0.000516 | -0.001254 | -0.002849 | -0.006155 | 0.000016 | -0.009272 | -0.013187 | -0.015317 | -0.015662 | -0.014967 | -0.009227 | 0.025373 | 0.049081 | -0.001592 | 0.008534 | -0.004011 | -0.006131 | 0.071942 | -0.010463 | 0.010224 | 0.016941 | 0.055896 | 0.088235 | 0.084986 | 0.102180 | 0.070966 | 0.010944 | 0.092762 | -0.036219 | -0.015724 | -0.099459 | -0.083461 | -0.050462 | 0.019262 | -0.100383 | -0.097887 | -0.096308 | -0.099094 | 0.041416 | -0.019879 | -0.056383 | -0.104982 | -0.027970 | 0.087403 | -0.001613 | -0.012517 | 0.088596 | -0.031808 | 0.094322 | -0.036517 | -0.043726 | 0.039763 | 0.044789 | -0.064166 | -0.103474 | -0.033358 | 0.027259 | -0.025306 | -0.019744 | -0.044603 | 0.012888 | -0.094829 | -0.094625 | -0.041227 | -0.053335 | -0.067892 | 0.022370 | -0.050605 | -0.013456 | -0.051233 | -0.103788 | -0.077216 | -0.099452 | -0.111264 | -0.117810 | 0.093961 | 0.089553 | 0.023501 | 0.070591 | 0.018706 | 0.039119 | -0.056989 | -0.037168 | 0.043876 | -0.002333 | 0.070589 | -0.013876 | 0.004464 | 0.012156 | 0.052247 | 0.086852 | 0.084383 | 0.101932 | 0.068955 | 0.008663 | 0.092922 | -0.043486 | -0.019348 | -0.101269 | -0.085413 | -0.055806 | 0.016646 | -0.100524 | -0.099004 | -0.097437 | -0.099144 | 0.040599 | -0.018370 | -0.055940 | -0.103951 | -0.033310 | 0.086692 | -0.005427 | -0.015858 | 0.087950 | -0.032558 | 0.093337 | -0.041952 | -0.048266 | 0.039260 | 0.041022 | -0.067203 | -0.102147 | -0.033188 | 0.024481 | -0.023861 | -0.020854 | -0.046268 | 0.009024 | -0.094621 | -0.094054 | -0.040291 | -0.056039 | -0.065157 | 0.022911 | -0.053729 | -0.017638 | -0.053793 | -0.105655 | -0.080008 | -0.098469 | -0.111256 | -0.118202 | 0.093789 | 0.088934 | 0.014826 | 0.068402 | 0.018833 | 0.044587 | -0.060631 | -0.054828 | 0.014479 | -0.011740 | 0.030154 | -0.041832 | -0.118923 | 0.080793 | -0.047940 | 0.093005 | 0.067507 | -0.062549 | -0.080377 | -0.093682 | 0.011350 | 0.010511 | -0.019762 | 0.054064 | 0.030386 | -0.071277 | 0.012321 | -0.094254 | -0.115470 | -0.011740 | -0.016480 | -0.001592 | -0.055622 | -0.026290 | -0.007574 | -0.055116 | -0.101505 | -0.105472 | 0.088038 | 0.090609 | -0.044032 | 0.086213 | -0.116322 | 0.088957 | -0.026186 | -0.023713 | -0.023713 | -0.024738 | -0.022661 | -0.019307 |
| 2 | 0.032090 | 0.003081 | 0.008313 | 0.016172 | 0.016107 | 0.028764 | -0.077737 | -0.077960 | -0.077859 | -0.091612 | 0.003081 | 0.002646 | 0.003561 | 0.016275 | 0.040054 | -0.049858 | -0.061223 | 0.064525 | 0.086860 | 0.037786 | 0.015549 | 0.011148 | 0.010890 | 0.010890 | 0.013322 | 0.008180 | 0.006992 | 0.005943 | 0.006476 | -0.000274 | -0.005955 | -0.023959 | -0.004520 | 0.006753 | -0.003744 | 0.007116 | -0.008720 | -0.006691 | -0.006329 | -0.003357 | 0.003477 | 0.011469 | 0.014992 | -0.020271 | 0.016807 | 0.016619 | 0.019803 | 0.024402 | 0.033593 | 0.050590 | 0.001376 | -0.003323 | -0.024446 | -0.000445 | 0.000996 | -0.014853 | 0.033545 | 0.037858 | -0.058942 | -0.001215 | 0.009746 | 0.022930 | 0.001435 | -0.003379 | -0.003579 | 0.000194 | 0.010974 | 0.007980 | -0.048277 | -0.025747 | 0.078591 | -0.027973 | -0.042775 | -0.031861 | -0.058942 | -0.075435 | 0.027691 | -0.058087 | -0.059783 | -0.077859 | -0.091921 | 0.002397 | 0.002713 | -0.023791 | -0.006228 | -0.007188 | -0.012136 | -0.010003 | -0.008991 | -0.005708 | 0.001410 | 0.009458 | 0.013994 | -0.020048 | 0.016063 | 0.014682 | 0.015421 | 0.018300 | 0.025710 | 0.037889 | -0.002312 | 0.004034 | -0.001162 | 0.101486 | 0.043127 | 0.103668 | 0.092603 | 0.057047 | 0.098614 | 0.072006 | 0.029761 | -0.008161 | -0.036387 | -0.012029 | -0.015629 | 0.011553 | -0.076563 | 0.003546 | 0.006537 | -0.001932 | 0.065062 | -0.043034 | 0.045816 | -0.034429 | 0.031930 | -0.041085 | 0.029334 | -0.039348 | -0.042746 | -0.041085 | 0.006572 | -0.042411 | -0.042411 | 0.081799 | -0.041946 | -0.042868 | -0.023873 | -0.005398 | 0.004626 | -0.005503 | 0.004972 | -0.010467 | -0.008372 | -0.007672 | -0.004527 | 0.002451 | 0.010478 | 0.014510 | -0.020158 | 0.016455 | 0.015716 | 0.017793 | 0.021683 | 0.030243 | 0.045972 | 0.020974 | -0.013808 | -0.000445 | 0.009770 | -0.018910 | -0.016641 | 0.055604 | -0.019444 | -0.111625 | 0.082507 | -0.001303 | 0.013034 | 0.097352 | 0.010728 | -0.011326 | -0.015736 | 0.051085 | -0.063356 | -0.080846 | -0.081298 | 0.081995 | 0.028746 | 0.118725 | 0.066486 | 0.077051 | 0.063482 | 0.105451 | -0.000352 | -0.028090 | 0.007668 | 0.091566 | -0.140576 | -0.019441 | -0.088123 | -0.128073 | -0.026115 | -0.119573 | 0.076317 | -0.010949 | -0.096141 | 0.111727 | 0.032838 | -0.001280 | 0.062667 | -0.026991 | 0.105633 | -0.010747 | -0.036067 | 0.068089 | 0.090062 | -0.079574 | -0.039653 | -0.090367 | -0.112380 | -0.064592 | 0.136751 | -0.123052 | -0.087573 | 0.037244 | 0.059572 | 0.022237 | 0.079540 | 0.025796 | 0.024162 | 0.050945 | -0.016170 | -0.103065 | 0.051743 | 0.118450 | -0.086586 | -0.006524 | 0.021003 | -0.011673 | 0.004626 | 0.053026 | -0.019005 | -0.113898 | 0.082707 | -0.003554 | 0.011146 | 0.099461 | 0.013610 | -0.011029 | -0.019594 | 0.048780 | -0.061826 | -0.081416 | -0.079944 | 0.081974 | 0.025344 | 0.120533 | 0.071711 | 0.079021 | 0.064313 | 0.105245 | 0.000466 | -0.027910 | 0.008417 | 0.092725 | -0.141759 | -0.020136 | -0.090154 | -0.128668 | -0.026564 | -0.120108 | 0.079377 | -0.013081 | -0.095964 | 0.112286 | 0.035718 | 0.001598 | 0.068500 | -0.026416 | 0.104955 | -0.011142 | -0.034554 | 0.066114 | 0.099710 | -0.076920 | -0.038922 | -0.086326 | -0.108512 | -0.064144 | 0.137573 | -0.123816 | -0.089430 | 0.038849 | 0.061339 | 0.027616 | 0.083932 | 0.033106 | 0.028594 | 0.049931 | -0.016009 | -0.109768 | 0.055062 | 0.118193 | -0.090465 | -0.022624 | -0.028570 | 0.045168 | 0.007981 | -0.001695 | -0.000489 | 0.057746 | 0.030119 | -0.036581 | 0.044748 | 0.048158 | -0.082089 | 0.093889 | 0.080423 | -0.111393 | 0.055943 | 0.021591 | 0.078770 | -0.031582 | 0.024465 | 0.007623 | -0.022470 | -0.006959 | 0.007981 | 0.008596 | -0.000445 | -0.098971 | 0.011121 | 0.016734 | 0.013036 | -0.041565 | -0.001591 | 0.008349 | 0.010803 | 0.003977 | 0.023723 | -0.012311 | 0.013686 | 0.006555 | 0.008716 | 0.008716 | 0.010216 | 0.007182 | 0.018853 |
| 3 | -0.006155 | -0.047969 | -0.000254 | -0.004334 | -0.004376 | 0.027317 | -0.042152 | -0.041447 | -0.041809 | -0.019525 | -0.047969 | -0.047092 | -0.048935 | 0.010362 | 0.146194 | -0.010287 | -0.004217 | 0.134147 | 0.189814 | 0.093224 | 0.002748 | 0.002926 | 0.002363 | 0.002363 | 0.000660 | 0.004253 | -0.008931 | -0.004438 | -0.008959 | -0.005501 | -0.012409 | -0.022309 | -0.019915 | -0.006062 | -0.019494 | -0.005253 | -0.018465 | -0.018718 | -0.016942 | -0.013434 | -0.009514 | -0.006287 | -0.003280 | -0.020423 | 0.001242 | 0.010229 | 0.017504 | 0.021941 | 0.023101 | 0.022084 | -0.009265 | -0.004943 | -0.022865 | -0.009664 | -0.011066 | -0.020331 | -0.022888 | 0.001111 | -0.003358 | -0.009359 | -0.020401 | -0.007694 | -0.006811 | 0.001988 | 0.007043 | 0.004048 | 0.000094 | -0.003046 | -0.039888 | -0.037061 | 0.031089 | -0.020490 | -0.044730 | -0.024271 | -0.003358 | 0.027415 | 0.000950 | -0.002142 | -0.004562 | -0.041809 | -0.020049 | -0.006743 | -0.005900 | -0.022266 | -0.019927 | -0.019534 | -0.017150 | -0.017661 | -0.016810 | -0.013914 | -0.009778 | -0.007283 | -0.005016 | -0.020905 | -0.001785 | 0.009560 | 0.021204 | 0.029632 | 0.035902 | 0.039008 | -0.010072 | -0.007005 | -0.006305 | 0.075748 | 0.139980 | 0.070865 | 0.042360 | 0.141307 | 0.078435 | 0.155997 | 0.096154 | -0.018306 | -0.012687 | -0.021383 | -0.021127 | -0.016204 | -0.034011 | -0.048131 | 0.000554 | -0.021317 | 0.019147 | -0.013559 | -0.004754 | -0.031757 | -0.012467 | -0.043820 | 0.026258 | -0.043958 | -0.043681 | -0.043820 | 0.007471 | -0.014642 | -0.014642 | 0.137306 | -0.015367 | -0.013916 | -0.022287 | -0.019922 | -0.006395 | -0.019516 | -0.005569 | -0.017794 | -0.018183 | -0.016877 | -0.013674 | -0.009646 | -0.006778 | -0.004121 | -0.020668 | -0.000197 | 0.009917 | 0.019206 | 0.025381 | 0.028586 | 0.028365 | 0.041546 | -0.021258 | -0.009664 | -0.017578 | -0.006601 | -0.025659 | -0.011353 | 0.033891 | 0.002805 | 0.054170 | -0.000644 | -0.016796 | -0.029490 | -0.073561 | 0.011651 | 0.010343 | -0.070535 | 0.036005 | 0.087234 | 0.008373 | 0.003913 | 0.025553 | 0.116382 | -0.020935 | 0.020989 | -0.019909 | -0.089950 | 0.061997 | -0.039389 | 0.016964 | -0.086284 | 0.029623 | -0.050788 | 0.089029 | -0.010764 | -0.032386 | 0.047983 | -0.032208 | 0.080246 | 0.089166 | -0.094979 | 0.090853 | 0.049068 | -0.082718 | 0.195664 | 0.058907 | 0.199108 | 0.173721 | 0.117937 | -0.001166 | -0.065312 | -0.094514 | -0.080855 | 0.035077 | -0.069519 | -0.056121 | 0.045844 | 0.104736 | 0.052091 | 0.004193 | 0.024630 | -0.074103 | -0.070036 | -0.028931 | -0.047287 | -0.060408 | -0.005888 | 0.009961 | -0.080290 | -0.022494 | -0.052943 | -0.023672 | -0.034677 | -0.006395 | -0.011440 | 0.032828 | 0.004564 | 0.055371 | 0.002478 | -0.017497 | -0.026184 | -0.075106 | 0.012620 | 0.011499 | -0.069699 | 0.032877 | 0.087607 | 0.001183 | 0.002847 | 0.024097 | 0.115283 | -0.025958 | 0.018690 | -0.022541 | -0.090125 | 0.063077 | -0.037893 | 0.017510 | -0.086672 | 0.029628 | -0.049803 | 0.090620 | -0.010091 | -0.029382 | 0.045939 | -0.030108 | 0.078099 | 0.085495 | -0.094622 | 0.093196 | 0.049138 | -0.086343 | 0.195697 | 0.060200 | 0.199201 | 0.175298 | 0.120710 | 0.001720 | -0.069856 | -0.098154 | -0.082756 | 0.031807 | -0.077156 | -0.055377 | 0.042269 | 0.104069 | 0.052880 | -0.006087 | 0.008904 | -0.076142 | -0.071180 | -0.037057 | -0.043436 | -0.059180 | -0.002454 | 0.013795 | -0.079867 | -0.028309 | -0.050283 | -0.002161 | -0.070956 | -0.003046 | 0.017808 | 0.031465 | -0.059071 | -0.054671 | -0.032490 | -0.029284 | -0.055731 | 0.042425 | 0.029325 | -0.057323 | 0.016255 | -0.001403 | 0.164185 | -0.065879 | -0.009064 | 0.022195 | 0.050779 | 0.009263 | -0.089657 | -0.003046 | 0.003692 | -0.009664 | -0.101031 | -0.014716 | -0.008299 | 0.060365 | -0.029720 | 0.029495 | -0.044447 | -0.048578 | 0.091377 | -0.047359 | -0.043359 | -0.034301 | 0.007493 | 0.005562 | 0.005562 | 0.006448 | 0.004655 | 0.005683 |
| 4 | -0.008597 | 0.017211 | -0.012733 | -0.013576 | -0.013243 | -0.003663 | 0.046590 | 0.045204 | 0.045911 | -0.055925 | 0.017211 | 0.018703 | 0.015560 | 0.022603 | -0.168747 | -0.078060 | -0.054355 | -0.162045 | -0.113371 | -0.056396 | 0.009111 | -0.011294 | 0.007543 | 0.007543 | 0.007419 | 0.007676 | -0.013265 | 0.001715 | -0.013815 | -0.006325 | -0.009482 | -0.023116 | -0.018770 | 0.000337 | -0.012778 | 0.001248 | -0.008391 | -0.009869 | -0.013597 | -0.011485 | -0.006375 | -0.002556 | 0.001037 | -0.023760 | 0.008764 | 0.019648 | 0.028539 | 0.033154 | 0.033254 | 0.026726 | -0.004739 | 0.002702 | -0.023419 | -0.005365 | -0.008002 | -0.021778 | -0.013627 | -0.011731 | -0.004825 | -0.005175 | 0.022096 | -0.027388 | 0.019121 | 0.012316 | 0.009339 | -0.002924 | -0.010882 | -0.003012 | 0.003575 | 0.041749 | 0.037616 | 0.014402 | 0.003342 | 0.040255 | -0.004825 | -0.014453 | 0.004269 | -0.004008 | -0.005633 | 0.045911 | -0.056254 | -0.000877 | -0.000008 | -0.022975 | -0.018050 | -0.012096 | -0.007847 | -0.008972 | -0.012439 | -0.011073 | -0.006481 | -0.003508 | -0.001830 | -0.023909 | 0.004274 | 0.016670 | 0.028889 | 0.037673 | 0.042740 | 0.044746 | -0.006007 | -0.001138 | -0.007947 | 0.114445 | -0.176215 | 0.117680 | 0.142872 | -0.173584 | 0.109373 | -0.152659 | -0.060321 | -0.010405 | 0.003267 | 0.017081 | 0.015596 | -0.001121 | 0.057809 | 0.016809 | 0.005564 | 0.017496 | 0.022014 | 0.023370 | -0.015896 | 0.007299 | -0.005285 | 0.003686 | 0.034618 | 0.004747 | 0.002666 | 0.003686 | -0.010207 | 0.025096 | 0.025096 | -0.144260 | 0.025219 | 0.024968 | -0.023044 | -0.018401 | -0.000256 | -0.012430 | 0.000636 | -0.008113 | -0.009415 | -0.013014 | -0.011280 | -0.006428 | -0.003025 | -0.000352 | -0.023836 | 0.006630 | 0.018259 | 0.028703 | 0.035180 | 0.037328 | 0.033419 | -0.049979 | 0.016349 | -0.005365 | 0.007035 | 0.002727 | 0.019080 | -0.019530 | -0.118887 | -0.080711 | -0.096373 | 0.031761 | 0.027145 | 0.010050 | 0.061061 | -0.040542 | -0.100661 | 0.042381 | -0.063219 | -0.033547 | 0.029744 | -0.040704 | -0.019747 | -0.109123 | -0.022691 | 0.039244 | -0.109578 | -0.058600 | 0.089820 | -0.116917 | -0.014023 | -0.065867 | -0.064111 | 0.062411 | 0.050934 | -0.114977 | 0.082107 | -0.079075 | 0.017148 | 0.020737 | -0.035608 | 0.034151 | -0.008905 | 0.047490 | -0.026527 | 0.160520 | 0.024531 | 0.168166 | 0.166375 | 0.071135 | 0.009042 | 0.047640 | 0.042366 | -0.018921 | 0.064635 | 0.116193 | 0.002635 | -0.060148 | 0.027425 | 0.055610 | 0.019440 | 0.011440 | -0.047201 | 0.040203 | 0.027668 | 0.038156 | 0.049925 | -0.058586 | 0.018342 | 0.019774 | -0.021324 | -0.044134 | -0.065348 | -0.003765 | -0.000256 | -0.021373 | -0.123220 | -0.087982 | -0.101902 | 0.031021 | 0.026945 | 0.005890 | 0.060968 | -0.044113 | -0.104463 | 0.042848 | -0.068494 | -0.038668 | 0.027978 | -0.041280 | -0.020361 | -0.109541 | -0.025014 | 0.038582 | -0.109541 | -0.058810 | 0.089594 | -0.113137 | -0.013018 | -0.066762 | -0.067651 | 0.060233 | 0.046920 | -0.118387 | 0.082582 | -0.083217 | 0.015438 | 0.017834 | -0.041346 | 0.033575 | -0.012720 | 0.045688 | -0.025029 | 0.160649 | 0.024782 | 0.167494 | 0.167288 | 0.072863 | 0.006039 | 0.048792 | 0.040811 | -0.013280 | 0.069398 | 0.112606 | 0.000003 | -0.067300 | 0.020960 | 0.053715 | 0.013426 | 0.002533 | -0.046207 | 0.040680 | 0.024884 | 0.036265 | 0.048336 | -0.070784 | 0.014260 | 0.019689 | -0.020158 | -0.036796 | -0.068038 | -0.004876 | -0.003012 | -0.066280 | -0.010118 | -0.001907 | 0.023294 | -0.049398 | 0.012805 | -0.008549 | -0.028384 | -0.036217 | -0.071715 | -0.025471 | 0.010444 | 0.133500 | -0.001523 | 0.008921 | -0.011950 | -0.024663 | 0.068124 | -0.001672 | -0.003012 | 0.004411 | -0.005365 | 0.181867 | 0.045235 | -0.002658 | 0.066810 | -0.008848 | -0.036772 | 0.053907 | 0.057481 | -0.027950 | 0.065311 | -0.043220 | 0.043804 | -0.010207 | -0.016435 | -0.016435 | -0.016171 | -0.016702 | 0.023654 |
# Top 10 loading scores of PC1
loading_scores = pd.Series(data=pca_statetest.components_[0], index = test_newstate_df.columns)
sorted_loading_scores = loading_scores.abs().sort_values(ascending=False)
top_10 = sorted_loading_scores[0:10].index.values
print(loading_scores[top_10])
MAL21UP_CY 0.068160 MAL18UP_CY 0.068157 MARBASE_CY 0.068141 POP21UP_CY 0.068141 TADULTBASE 0.068141 POP18UP_CY 0.068141 EDUCBASECY 0.068133 HHPOP_CY 0.068126 RACEBASECY 0.068125 GENBASE_CY 0.068125 dtype: float64
These loading score values are very similar which means a lot of the variables played a role in creating the principal component. The loading score details can be seen in dataframe of components above. The composition also tells us that our component is not depend on a few variables but many variables were used.
sns.set(rc={'figure.figsize':(32,8.27)})
sns.heatmap(pca_statetest.components_[0:4], xticklabels=test_newstate_df.columns, annot=True)
<matplotlib.axes._subplots.AxesSubplot at 0x1f4bbe41320>
the value 0.144 for Feature Provider Count is the score of this feature on the PC1. This value tells us 'how much' the feature influences the PC (in our case the PC1).
So the higher the value, the higher the influence on the principal component.
from sklearn.preprocessing import PowerTransformer
pt_state = PowerTransformer(method='yeo-johnson')
newstate_df_pt = pt_state.fit_transform(test_newstate_df)
C:\Users\mohi9282\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\sklearn\preprocessing\data.py:2863: RuntimeWarning: divide by zero encountered in log loglike = -n_samples / 2 * np.log(x_trans.var())
# Import and fit PCA
from sklearn.decomposition import PCA
# pca_statetest = PCA().fit(newstate_df_std)
# pca_newstate = pca_statetest.transform(newstate_df_std)
pca_statetest = PCA(n_components=8)
pca_newstate = pca_statetest.fit_transform(newstate_df_pt)
pca_statetest.components_
array([[ 8.56827471e-02, 2.46501764e-17, 1.70612740e-17, ...,
8.56642910e-02, 8.58807499e-02, 8.59396494e-02],
[ 2.04009326e-02, -4.51530494e-17, 4.92165744e-17, ...,
-1.89815983e-02, -1.92861618e-02, -1.18518428e-02],
[-3.69484764e-02, 1.68812862e-17, -8.88588834e-17, ...,
-2.92403533e-02, -2.52796280e-02, -4.20536595e-02],
...,
[ 9.81709512e-03, -1.48631479e-16, 6.07594257e-17, ...,
6.00700145e-03, 4.28828440e-03, 9.54607887e-03],
[ 1.18446865e-02, 1.38316898e-17, 9.84220915e-20, ...,
-2.12025122e-02, -1.56032701e-02, 1.48317374e-02],
[ 6.02476248e-04, -1.75361435e-16, -6.85856629e-17, ...,
-1.57583965e-02, -1.39048380e-02, 2.27678656e-02]])
pca_statetest.explained_variance_ratio_
array([0.57343958, 0.12373149, 0.07294693, 0.04454296, 0.02618836,
0.01954974, 0.01720015, 0.01370818])
sum(pca_statetest.explained_variance_ratio_[:5])
0.8408493275144913
# Show percentage of variance explained by different principal components
sns.set(rc={'figure.figsize':(7,7)})
labels = ['PC' + str(x) for x in range(1,9)]
plt.plot(np.cumsum(pca_statetest.explained_variance_ratio_))
plt.bar(x=range(0,8), height=pca_statetest.explained_variance_ratio_, tick_label = labels)
plt.xlabel('Number of components')
# plt.yticks(range(0,1,0.1))
# plt.ylim(bottom=0.90)
plt.ylabel('Cumulative explained variance');
The variance explained by PC1 is ~57% and the cumulative variance explained by first 5 components is ~84%.
# Create df for PCs with State Labels
pca_df = pd.DataFrame(data=pca_newstate, index=newstate_obgyn_df.RegionAbbr, columns = ['PC1','PC2','PC3','PC4','PC5','PC6','PC7','PC8'])
pca_df
| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 | |
|---|---|---|---|---|---|---|---|---|
| RegionAbbr | ||||||||
| CA | 24.941061 | 6.474051 | 5.609687 | -0.962691 | -0.704153 | 0.894702 | 0.912505 | -0.478776 |
| TX | 23.908717 | -2.544515 | 5.188710 | 0.814537 | 1.263244 | -0.390794 | 0.796420 | -1.510516 |
| NY | 16.397040 | 4.697254 | -4.149995 | -0.055749 | 0.600847 | 5.558235 | 3.165286 | -0.460776 |
| FL | 21.575314 | -0.545527 | -1.160204 | -1.382744 | -4.993933 | 0.063733 | -2.346939 | 0.841382 |
| PA | 14.254328 | -0.192569 | -7.431125 | -2.612835 | 0.542363 | 2.054582 | 2.224964 | -1.109572 |
| IL | 13.903033 | 1.648756 | -2.133280 | 1.190061 | 3.743880 | 0.313898 | 1.203285 | -0.065390 |
| MI | 10.537042 | -4.242443 | -4.319592 | -1.417743 | 0.299792 | 3.046424 | -0.083627 | 0.379641 |
| OH | 11.340141 | -5.463046 | -5.949948 | -0.040940 | 2.577762 | 0.054191 | 0.904389 | 0.531492 |
| NJ | 6.359957 | 11.923885 | -1.803437 | 2.064464 | 0.032182 | 0.124552 | -0.960482 | -0.039266 |
| GA | 10.837933 | -2.252931 | 1.608639 | 4.604972 | -0.408737 | -2.809584 | -1.003404 | -1.468180 |
| NC | 11.626048 | -4.611761 | 0.913803 | 1.403094 | -2.432750 | -0.588660 | 0.045871 | -1.422783 |
| VA | 9.744681 | 2.424011 | -0.872190 | 1.231040 | 0.159863 | -3.181201 | 3.037987 | -1.743821 |
| MA | 4.507952 | 11.264660 | -4.467088 | -1.673932 | 0.368888 | 1.671717 | 0.221221 | -1.042171 |
| MD | 4.452790 | 7.260558 | -1.979991 | 2.836840 | 1.174696 | -3.648621 | -0.409089 | -1.969792 |
| MO | 4.902518 | -4.333476 | -2.138973 | -0.195669 | 0.461320 | -1.158055 | 1.765426 | -0.031696 |
| WA | 9.147755 | 4.068888 | 3.896582 | -4.377601 | -1.041934 | -1.104182 | 2.407585 | -0.373697 |
| AZ | 8.454773 | 1.349003 | 6.549264 | 0.354253 | -2.795823 | 2.677352 | -3.556716 | 1.030261 |
| IN | 3.858799 | -6.668229 | -2.547097 | 0.339778 | 2.779614 | -0.741004 | -1.179909 | 2.625238 |
| TN | 5.239647 | -5.048140 | -1.881375 | 2.747517 | -1.342516 | -2.050955 | -0.197252 | 0.895178 |
| CO | 5.947022 | 3.222646 | 4.968447 | -2.856916 | 1.318518 | -0.989827 | -1.125881 | -0.443467 |
| SC | 1.718689 | -4.691551 | -0.619011 | 3.274218 | -3.414196 | -2.499577 | -0.623431 | 0.431597 |
| CT | -2.294625 | 10.431394 | -4.872230 | 0.092104 | 0.589346 | -0.159243 | -3.347191 | -1.503408 |
| LA | 1.056402 | -3.511091 | -0.672560 | 4.889602 | -0.827736 | -0.069310 | -0.055843 | -2.734135 |
| MN | 4.113591 | -0.888401 | -0.001173 | -5.122606 | 3.598770 | -1.222785 | -1.959676 | -1.393251 |
| WI | 3.767021 | -3.213935 | -3.497921 | -4.185546 | 3.481066 | -0.207978 | -1.939934 | 0.580935 |
| KY | -0.522537 | -6.035116 | -2.311867 | 1.603457 | -0.983441 | -1.515464 | 0.491492 | 0.730095 |
| AL | 0.925128 | -5.318121 | -1.607998 | 4.253343 | -1.751460 | -1.038961 | 0.146819 | -0.091159 |
| OR | 2.338167 | 2.398451 | 2.504058 | -6.288424 | -2.015892 | -0.349357 | -1.258296 | 1.892730 |
| OK | 0.294842 | -6.532883 | 3.154125 | 1.527624 | 0.665162 | 2.570006 | 0.153812 | 0.534761 |
| MS | -5.866029 | -5.675623 | -0.737126 | 6.285331 | -1.565272 | -0.573433 | 0.422939 | -1.234640 |
| NV | -0.838720 | 3.135504 | 6.268458 | 1.763869 | -1.500820 | 0.511188 | -2.649213 | 3.706389 |
| UT | -2.881539 | 2.169321 | 8.881553 | -0.298284 | 2.595103 | -3.469909 | 0.240853 | 1.380089 |
| KS | -2.902079 | -3.822638 | 1.551843 | 0.804892 | 4.384112 | 1.185009 | -0.163252 | 1.509428 |
| IA | -4.339032 | -4.497665 | -1.166603 | -2.728882 | 4.526969 | -0.478473 | -0.095412 | 3.695027 |
| AR | -2.980338 | -5.599416 | 1.106490 | 3.101318 | -2.470753 | -0.025113 | 0.099192 | 2.347363 |
| NM | -4.288426 | -0.981584 | 6.037603 | -0.322682 | -3.979422 | 5.608560 | -2.521629 | -0.211333 |
| HI | -10.161014 | 10.925445 | 2.845990 | 0.886577 | -1.758315 | -1.406064 | 7.962511 | 5.360556 |
| NE | -7.821808 | -2.790074 | 1.732173 | 0.295374 | 5.522137 | 0.515462 | -1.494783 | 1.140025 |
| WV | -12.870474 | -5.544047 | -6.308938 | -0.439109 | -4.314826 | 0.774787 | 1.414517 | 0.949638 |
| DC | -20.917248 | 9.532703 | -3.460152 | 10.923909 | 2.896917 | 3.413946 | -0.107010 | 0.506364 |
| RI | -12.154689 | 9.182793 | -3.332281 | -0.125686 | -0.243982 | 1.736370 | -3.376211 | 1.001130 |
| NH | -12.059679 | 3.929578 | -3.747129 | -5.235653 | -1.495788 | -3.763940 | -1.271458 | -0.821153 |
| ID | -8.787058 | -2.575458 | 4.857307 | -2.429535 | 0.472843 | 0.120727 | 0.485380 | 1.504048 |
| DE | -11.754908 | 4.623053 | -2.260126 | 1.744238 | -1.619071 | -3.778306 | -3.127486 | 0.162652 |
| ME | -12.797534 | -0.946150 | -4.928566 | -5.514554 | -4.260834 | -0.207112 | 0.788516 | -0.327974 |
| MT | -13.697272 | -4.371800 | 1.044493 | -2.815766 | -2.016772 | 3.464333 | 1.947298 | -1.906533 |
| AK | -15.516930 | 1.766643 | 10.256209 | -0.055205 | 0.214877 | -0.055887 | 2.102388 | -4.804770 |
| VT | -19.784226 | -0.583679 | -5.201189 | -3.978771 | -1.249397 | -1.050658 | 0.374199 | -0.682257 |
| SD | -15.690296 | -4.442951 | 1.937501 | -1.077048 | 1.393601 | 1.940808 | 0.448964 | -1.634963 |
| WY | -18.359159 | -0.995520 | 2.587292 | -1.794674 | 0.298392 | -0.320102 | 1.084521 | -1.805239 |
| ND | -16.864769 | -3.508255 | 2.058941 | -1.043167 | 3.225556 | 0.553975 | 0.005788 | -2.425301 |
plt.figure(figsize=(20,10))
per_var = np.round(pca_statetest.explained_variance_ratio_*100, decimals=1)
plt.scatter(pca_df.PC1, pca_df.PC2)
plt.xlabel('PC1 - {0}%'.format(per_var[0]))
plt.ylabel('PC2 - {0}%'.format(per_var[1]))
for sample in pca_df.index:
plt.annotate(sample, (pca_df.PC1.loc[sample],pca_df.PC2.loc[sample]))
This graph of first 2 components suggest that the cluster of states on the left (DC, UT, AK, CT) are correlated with each other. Similarly states in the bottom center (GA, NC, MI etc) are correlated with each other. The separation of 2 clusters along x-axis suggests that states towards the left are very different from states towards the right.
componentdf = pd.DataFrame(data=pca_statetest.components_, columns = test_newstate_df.columns)
componentdf.head()
| Provider_Count | AAGEBASECY | AGGDI_CY | AGGHINC_CY | AGGINC_CY | AGGNW_CY | AIFBASE_CY | AIMBASE_CY | AMERIND_CY | AREA | ASIAN_CY | ASNFBASECY | ASNMBASECY | ASSCDEG_CY | AVGDI_CY | AVGFMSZ_CY | AVGHHSZ_CY | AVGHINC_CY | AVGNW_CY | AVGVAL_CY | BABYBOOMCY | BACHDEG_CY | BAGEBASECY | BLACK_CY | BLKFBASECY | BLKMBASECY | CIVLBFR_CY | EDUCBASECY | EMP_CY | FAMHH_CY | FAMPOP_CY | FEM0_CY | FEM15_CY | FEM18UP_CY | FEM20_CY | FEM21UP_CY | FEM25_CY | FEM30_CY | FEM35_CY | FEM40_CY | FEM45_CY | FEM50_CY | FEM55_CY | FEM5_CY | FEM60_CY | FEM65_CY | FEM70_CY | FEM75_CY | FEM80_CY | FEM85_CY | FEMALES_CY | GED_CY | GENALPHACY | GENBASE_CY | GENX_CY | GENZ_CY | GQPOP_CY | GRADDEG_CY | HAGEBASECY | HHPOP_CY | HINC0_CY | HINC150_CY | HINC15_CY | HINC25_CY | HINC35_CY | HINC50_CY | HINC75_CY | HINCBASECY | HISPAI_CY | HISPASN_CY | HISPBLK_CY | HISPMLT_CY | HISPOTH_CY | HISPPI_CY | HISPPOP_CY | HISPWHT_CY | HSGRAD_CY | HSPFBASECY | HSPMBASECY | IAGEBASECY | LANDAREA | MAL18UP_CY | MAL21UP_CY | MALE0_CY | MALE15_CY | MALE20_CY | MALE25_CY | MALE30_CY | MALE35_CY | MALE40_CY | MALE45_CY | MALE50_CY | MALE55_CY | MALE5_CY | MALE60_CY | MALE65_CY | MALE70_CY | MALE75_CY | MALE80_CY | MALE85_CY | MALES_CY | MARBASE_CY | MARRIED_CY | MEDAGE_CY | MEDDI_CY | MEDFAGE_CY | MEDHHR_CY | MEDHINC_CY | MEDMAGE_CY | MEDNW_CY | MEDVAL_CY | MILLENN_CY | MINORITYCY | MLTFBASECY | MLTMBASECY | NEVMARR_CY | NHSPAI_CY | NHSPASN_CY | NHSPBLK_CY | NHSPMLT_CY | NHSPOTH_CY | NHSPPI_CY | NHSPWHT_CY | NOHS_CY | NONHISP_CY | OAGEBASECY | OLDRGENSCY | OTHFBASECY | OTHMBASECY | OTHRACE_CY | OWNER_CY | PACIFIC_CY | PAGEBASECY | PCI_CY | PIFBASE_CY | PIMBASE_CY | POP0_CY | POP15_CY | POP18UP_CY | POP20_CY | POP21UP_CY | POP25_CY | POP30_CY | POP35_CY | POP40_CY | POP45_CY | POP50_CY | POP55_CY | POP5_CY | POP60_CY | POP65_CY | POP70_CY | POP75_CY | POP80_CY | POP85_CY | POPDENS_CY | RACE2UP_CY | RACEBASECY | RENTER_CY | SMCOLL_CY | SOMEHS_CY | TADULT01 | TADULT02 | TADULT03 | TADULT04 | TADULT05 | TADULT06 | TADULT07 | TADULT08 | TADULT09 | TADULT11 | TADULT12 | TADULT13 | TADULT14 | TADULT15 | TADULT16 | TADULT17 | TADULT18 | TADULT19 | TADULT20 | TADULT21 | TADULT22 | TADULT23 | TADULT24 | TADULT25 | TADULT26 | TADULT27 | TADULT28 | TADULT29 | TADULT30 | TADULT31 | TADULT32 | TADULT33 | TADULT34 | TADULT35 | TADULT36 | TADULT37 | TADULT38 | TADULT39 | TADULT40 | TADULT41 | TADULT42 | TADULT43 | TADULT44 | TADULT45 | TADULT46 | TADULT47 | TADULT48 | TADULT49 | TADULT50 | TADULT51 | TADULT52 | TADULT53 | TADULT54 | TADULT55 | TADULT56 | TADULT57 | TADULT58 | TADULT59 | TADULT60 | TADULT61 | TADULT62 | TADULT63 | TADULT64 | TADULT65 | TADULT66 | TADULT67 | TADULT68 | TADULTBASE | THH01 | THH02 | THH03 | THH04 | THH05 | THH06 | THH07 | THH08 | THH09 | THH11 | THH12 | THH13 | THH14 | THH15 | THH16 | THH17 | THH18 | THH19 | THH20 | THH21 | THH22 | THH23 | THH24 | THH25 | THH26 | THH27 | THH28 | THH29 | THH30 | THH31 | THH32 | THH33 | THH34 | THH35 | THH36 | THH37 | THH38 | THH39 | THH40 | THH41 | THH42 | THH43 | THH44 | THH45 | THH46 | THH47 | THH48 | THH49 | THH50 | THH51 | THH52 | THH53 | THH54 | THH55 | THH56 | THH57 | THH58 | THH59 | THH60 | THH61 | THH62 | THH63 | THH64 | THH65 | THH66 | THH67 | THH68 | THHBASE | THHGRPL1 | THHGRPL11 | THHGRPL12 | THHGRPL13 | THHGRPL14 | THHGRPL2 | THHGRPL3 | THHGRPL4 | THHGRPL5 | THHGRPL6 | THHGRPL7 | THHGRPL8 | THHGRPL9 | THHGRPU1 | THHGRPU2 | THHGRPU3 | THHGRPU4 | THHGRPU5 | THHGRPU6 | TOTHH_CY | TOTHU_CY | TOTPOP_CY | TSEGNUM | UNEMPRT_CY | UNEMP_CY | VACANT_CY | VAL0_CY | VAL150K_CY | VAL1M_CY | VAL1PT5MCY | VAL250K_CY | VAL2M_CY | VAL50K_CY | VAL750K_CY | VALBASE_CY | WAGEBASECY | WHITE_CY | WHTFBASECY | WHTMBASECY | WIDOWED_CY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.085683 | 2.465018e-17 | 1.706127e-17 | 1.206350e-17 | -2.418636e-17 | -5.895328e-18 | 0.052559 | 0.053578 | 0.053072 | 0.021408 | 5.023896e-18 | -3.009757e-18 | 0.078875 | 0.086244 | 3.655364e-18 | 0.039666 | 0.039064 | -3.754861e-18 | 6.154518e-18 | -2.263590e-18 | 3.418718e-20 | 3.215796e-18 | 0.074896 | 0.074896 | 0.074241 | 0.075580 | 2.357317e-18 | 1.196090e-18 | -7.261180e-18 | 6.962755e-18 | -1.683286e-18 | 4.051243e-18 | -4.713840e-18 | 1.615958e-18 | 9.115194e-19 | 1.294890e-18 | -5.218822e-18 | -5.617389e-18 | 3.388000e-18 | -5.696340e-18 | -2.240383e-18 | -1.960504e-18 | 9.940268e-19 | 9.715866e-19 | 3.287971e-18 | -1.923931e-18 | 1.941226e-19 | 0.086474 | 0.085888 | 0.084687 | -0.0 | 0.084626 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | 0.084138 | -0.0 | 0.085197 | 0.085924 | 0.086732 | -0.0 | -0.0 | -0.0 | 0.072163 | 0.076025 | 0.078033 | 0.078956 | 0.079231 | 0.073096 | -0.0 | -0.0 | 0.085892 | -0.0 | -0.0 | 0.053072 | 0.021303 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | 0.086432 | 0.085421 | -0.0 | -0.0 | -0.0 | -0.011538 | -0.0 | -0.008828 | -0.011955 | -0.0 | -0.015808 | 0.002422 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | 0.043703 | -0.0 | 0.074476 | -0.0 | 0.080397 | 0.059972 | 0.084554 | -0.0 | 0.086238 | 0.079535 | 0.086322 | 0.079111 | 0.079858 | 0.079535 | 0.087039 | 0.062084 | 0.062084 | -0.0 | 0.062408 | 0.061736 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | 0.086152 | 0.085001 | 0.031455 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | 0.073387 | 0.079703 | 0.069097 | 0.074758 | 0.080037 | 0.067452 | 0.047312 | 0.050130 | 0.070482 | 0.073022 | 0.040283 | 0.080947 | 0.078418 | 0.057543 | 0.079927 | 0.075710 | 0.055159 | 0.064269 | 0.073370 | 0.069975 | 0.056940 | 0.046094 | 0.014159 | 0.059571 | 0.047669 | 0.054627 | 0.059226 | 0.075081 | 0.066858 | 0.045659 | 0.056298 | 0.042672 | 0.074924 | 0.072042 | 0.051459 | 0.065963 | 0.076449 | 0.069033 | 0.066245 | 0.073411 | 0.069773 | 0.065770 | 0.076703 | 0.077049 | 0.049963 | 0.054998 | 0.031138 | 0.059298 | 0.053029 | 0.044205 | 0.072131 | 0.076982 | 0.071014 | 0.061065 | 0.063811 | 0.054572 | 0.063491 | 0.053655 | 0.052528 | 0.044027 | 0.072151 | 0.060901 | 0.050955 | 0.033574 | 0.075765 | 0.074839 | 0.062812 | -0.0 | 0.073953 | 0.079825 | 0.069087 | 0.074957 | 0.080305 | 0.068421 | 0.047543 | 0.050100 | 0.070942 | 0.072983 | 0.040673 | 0.080895 | 0.078635 | 0.056886 | 0.079575 | 0.075715 | 0.055016 | 0.063674 | 0.072955 | 0.069331 | 0.056168 | 0.045753 | 0.013229 | 0.059014 | 0.046911 | 0.053891 | 0.059649 | 0.075547 | 0.066238 | 0.045997 | 0.056547 | 0.042827 | 0.075695 | 0.072284 | 0.051457 | 0.066301 | 0.076254 | 0.068873 | 0.066576 | 0.073935 | 0.069686 | 0.065778 | 0.076579 | 0.076035 | 0.049554 | 0.054197 | 0.030470 | 0.059348 | 0.051329 | 0.044494 | 0.071894 | 0.077392 | 0.070773 | 0.061195 | 0.063641 | 0.054345 | 0.062992 | 0.053647 | 0.052797 | 0.044400 | 0.072230 | 0.060857 | 0.050516 | 0.030823 | 0.073175 | 0.070365 | 0.060398 | -0.0 | -0.0 | 0.082254 | 0.064420 | 0.071520 | 0.075812 | 0.065361 | 0.070086 | 0.078674 | 0.078209 | 0.062453 | 0.072502 | 0.084328 | 0.078787 | 0.070914 | -0.0 | 0.083569 | 0.084980 | 0.070164 | 0.063236 | -0.0 | -0.0 | -0.0 | -0.001014 | 0.023665 | -0.0 | -0.0 | 0.070085 | 0.079827 | 0.074140 | 0.068529 | 0.085044 | 0.068394 | 0.067000 | 0.072713 | 0.087039 | 0.085779 | 0.085779 | 0.085664 | 0.085881 | 0.085940 |
| 1 | 0.020401 | -4.515305e-17 | 4.921657e-17 | -5.089035e-17 | 1.364893e-17 | -2.587692e-17 | -0.034574 | -0.034343 | -0.034468 | -0.082945 | -2.002968e-17 | 8.590029e-18 | 0.065508 | -0.014635 | -1.808775e-19 | 0.071206 | 0.045470 | 2.557926e-18 | -1.558028e-17 | -1.449645e-17 | -3.559930e-18 | -2.388000e-18 | -0.004013 | -0.004013 | -0.003847 | -0.004269 | -1.557907e-18 | -4.505577e-18 | 3.060224e-18 | -1.475331e-17 | -7.810819e-18 | -4.422439e-18 | -2.418346e-18 | -5.500972e-18 | -9.763423e-18 | -6.925259e-18 | -1.613203e-18 | -3.095488e-18 | -3.172126e-18 | -7.212717e-18 | -2.898549e-18 | -2.690478e-18 | 1.752402e-18 | 2.462416e-18 | 2.815774e-19 | 3.954307e-19 | 4.885119e-19 | -0.006136 | -0.005213 | 0.001470 | 0.0 | -0.033630 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.026700 | 0.0 | -0.032828 | -0.031828 | -0.027269 | 0.0 | 0.0 | 0.0 | 0.029376 | 0.068137 | 0.058924 | 0.058497 | 0.040152 | 0.053106 | 0.0 | 0.0 | -0.013840 | 0.0 | 0.0 | -0.034468 | -0.082883 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.005139 | 0.003537 | 0.0 | 0.0 | 0.0 | 0.004787 | 0.0 | 0.004835 | 0.001448 | 0.0 | 0.005028 | 0.090090 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.057929 | 0.0 | -0.006618 | 0.0 | 0.056056 | 0.018270 | -0.023252 | 0.0 | -0.010651 | 0.041177 | -0.004183 | 0.044295 | 0.038425 | 0.041177 | -0.014953 | 0.027291 | 0.027291 | 0.0 | 0.028773 | 0.025898 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.005171 | 0.002221 | 0.096918 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.077742 | 0.019375 | -0.020860 | 0.051739 | 0.040435 | 0.099562 | 0.134863 | 0.096832 | 0.056042 | 0.053330 | 0.134557 | -0.019198 | 0.015402 | -0.115544 | -0.028065 | -0.034211 | 0.087488 | -0.099554 | -0.071594 | -0.053730 | -0.097189 | 0.029909 | -0.108946 | -0.042895 | -0.131720 | -0.078684 | 0.092232 | 0.014548 | -0.048333 | 0.030158 | -0.000352 | 0.137934 | -0.002385 | -0.026550 | 0.062636 | 0.067654 | -0.062313 | -0.095890 | 0.041771 | 0.080823 | 0.037290 | 0.001283 | 0.012376 | 0.044967 | -0.119622 | -0.102339 | -0.099934 | -0.086832 | -0.094661 | 0.089435 | -0.037392 | 0.004218 | -0.041333 | -0.035743 | -0.026033 | -0.123738 | -0.110088 | -0.064709 | 0.130808 | 0.073489 | 0.036109 | 0.083327 | 0.087660 | 0.033120 | -0.070204 | -0.023338 | 0.052308 | 0.0 | 0.075668 | 0.017482 | -0.023278 | 0.049717 | 0.037493 | 0.096809 | 0.134909 | 0.096946 | 0.055380 | 0.051508 | 0.134445 | -0.023970 | 0.012813 | -0.117875 | -0.030395 | -0.038521 | 0.085433 | -0.100777 | -0.073276 | -0.056165 | -0.098673 | 0.029621 | -0.109891 | -0.043018 | -0.132243 | -0.081836 | 0.091990 | 0.011743 | -0.050315 | 0.029866 | 0.000030 | 0.138497 | -0.005137 | -0.029566 | 0.063132 | 0.064886 | -0.063504 | -0.095880 | 0.041540 | 0.078440 | 0.038162 | 0.002199 | 0.011252 | 0.047321 | -0.120193 | -0.103700 | -0.100297 | -0.088392 | -0.097204 | 0.088955 | -0.041181 | 0.002230 | -0.043942 | -0.036755 | -0.027361 | -0.124069 | -0.111245 | -0.065343 | 0.130624 | 0.073384 | 0.033686 | 0.083089 | 0.092768 | 0.034786 | -0.078677 | -0.047789 | 0.025388 | 0.0 | 0.0 | -0.002094 | -0.096182 | 0.090065 | -0.062050 | 0.107495 | 0.077462 | -0.050829 | -0.025066 | -0.087196 | -0.028302 | 0.016160 | 0.046559 | 0.090392 | 0.0 | -0.039740 | 0.039568 | -0.107237 | -0.114634 | 0.0 | 0.0 | 0.0 | -0.123445 | 0.017914 | 0.0 | 0.0 | -0.105495 | -0.061881 | 0.079118 | 0.094034 | 0.005818 | 0.094706 | -0.110719 | 0.091677 | -0.014961 | -0.019127 | -0.019127 | -0.018982 | -0.019286 | -0.011852 |
| 2 | -0.036948 | 1.688129e-17 | -8.885888e-17 | -6.544145e-17 | 7.119450e-17 | 1.401133e-17 | 0.138857 | 0.138144 | 0.138520 | 0.169852 | -2.357290e-17 | -1.765870e-17 | 0.005683 | -0.015150 | 1.381044e-18 | 0.147914 | 0.138066 | -1.786496e-18 | -7.245192e-18 | 3.711866e-18 | 4.888289e-18 | 1.631003e-18 | -0.057173 | -0.057173 | -0.059538 | -0.054682 | -7.193773e-18 | -4.058091e-18 | -1.184503e-17 | 6.308261e-18 | -1.468394e-17 | 7.385927e-18 | -1.569572e-17 | 4.501841e-18 | 4.314889e-18 | -1.053572e-18 | 9.609118e-19 | -7.415317e-19 | 6.003387e-18 | 1.111243e-18 | 2.451795e-17 | 1.268278e-17 | -5.597962e-18 | 2.461913e-18 | -9.544324e-18 | 4.485265e-18 | -2.678288e-19 | -0.039549 | -0.046468 | -0.056877 | 0.0 | -0.024692 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.038479 | 0.0 | -0.032883 | -0.028070 | -0.021975 | 0.0 | 0.0 | 0.0 | 0.100647 | 0.057376 | -0.030139 | 0.055825 | 0.053661 | 0.071092 | 0.0 | 0.0 | -0.041263 | 0.0 | 0.0 | 0.138520 | 0.170232 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.037397 | -0.044245 | 0.0 | 0.0 | 0.0 | -0.181368 | 0.0 | -0.184369 | -0.153420 | 0.0 | -0.175238 | -0.024327 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.142495 | 0.0 | -0.057626 | 0.0 | -0.024296 | 0.128635 | -0.042146 | 0.0 | -0.035081 | 0.050818 | -0.041496 | 0.050484 | 0.051163 | 0.050818 | -0.027854 | 0.123618 | 0.123618 | 0.0 | 0.121862 | 0.125225 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.042634 | -0.052407 | -0.143522 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -0.024503 | 0.003177 | 0.103819 | -0.031631 | -0.006955 | 0.001720 | -0.027489 | -0.017360 | 0.013017 | -0.022303 | 0.003014 | 0.035865 | 0.061208 | 0.043367 | -0.060645 | -0.012981 | -0.063873 | -0.037757 | -0.068868 | -0.067396 | -0.107239 | 0.026627 | 0.055607 | 0.006354 | -0.075435 | 0.140275 | 0.046866 | 0.092063 | 0.089711 | 0.082724 | 0.111225 | 0.000296 | 0.012145 | 0.089855 | -0.034275 | -0.013408 | 0.007987 | -0.025098 | 0.005127 | -0.069318 | -0.006447 | 0.032874 | -0.061916 | -0.054699 | -0.002003 | -0.037959 | 0.070259 | 0.082087 | -0.023162 | -0.091292 | 0.078742 | 0.058526 | -0.004982 | -0.087630 | -0.078602 | -0.026438 | -0.058690 | -0.088308 | 0.004454 | 0.067604 | 0.052954 | -0.044996 | -0.022442 | 0.112413 | -0.018657 | -0.060983 | 0.025741 | 0.0 | -0.024553 | 0.001385 | 0.102382 | -0.033776 | -0.008540 | 0.002124 | -0.028443 | -0.017153 | 0.011107 | -0.020837 | 0.002355 | 0.032978 | 0.059259 | 0.040632 | -0.062508 | -0.014503 | -0.066097 | -0.041458 | -0.069693 | -0.069705 | -0.108260 | 0.026381 | 0.054180 | 0.005412 | -0.076570 | 0.138952 | 0.046258 | 0.090687 | 0.090814 | 0.082591 | 0.111279 | -0.001429 | 0.011623 | 0.086060 | -0.034118 | -0.016984 | 0.005907 | -0.028557 | 0.004776 | -0.068891 | -0.006276 | 0.032151 | -0.062059 | -0.056985 | -0.004664 | -0.040064 | 0.068663 | 0.079716 | -0.022534 | -0.091949 | 0.076612 | 0.056585 | -0.002580 | -0.089692 | -0.080588 | -0.029529 | -0.061226 | -0.089821 | 0.003343 | 0.066910 | 0.055397 | -0.045505 | -0.018725 | 0.119030 | -0.012483 | -0.041485 | -0.021118 | 0.0 | 0.0 | 0.000061 | -0.082621 | -0.007645 | 0.005437 | -0.018426 | -0.023834 | 0.045090 | -0.069725 | -0.064967 | 0.120359 | -0.000135 | -0.037833 | -0.014705 | 0.0 | -0.023279 | -0.010126 | 0.005014 | -0.052907 | 0.0 | 0.0 | 0.0 | 0.041781 | 0.009035 | 0.0 | 0.0 | -0.021272 | -0.040786 | -0.018901 | -0.006572 | -0.017319 | -0.019541 | -0.049012 | -0.001518 | -0.027858 | -0.027286 | -0.027286 | -0.029240 | -0.025280 | -0.042054 |
| 3 | 0.034192 | -3.086556e-17 | -1.398148e-17 | -2.235588e-17 | 1.655388e-17 | -1.331722e-16 | -0.040741 | -0.042075 | -0.041402 | -0.079849 | 4.623954e-17 | -1.805611e-17 | 0.011894 | -0.032501 | 2.771436e-17 | 0.060366 | 0.000587 | -1.593049e-17 | 2.424793e-17 | 3.309880e-17 | 3.082814e-17 | -2.268440e-17 | 0.138184 | 0.138184 | 0.140570 | 0.135368 | 1.546415e-18 | -1.437585e-17 | -2.140256e-18 | 1.256237e-17 | -1.703816e-17 | 4.748225e-18 | -9.666831e-18 | -9.665212e-19 | -1.956447e-17 | -1.827287e-17 | -5.064333e-18 | -9.547341e-18 | -9.287188e-18 | -2.172381e-17 | 7.173587e-18 | 1.063483e-18 | 3.963385e-18 | 3.658758e-18 | 1.258421e-18 | -1.892552e-18 | -1.944177e-20 | 0.007884 | 0.004274 | -0.007940 | -0.0 | 0.019728 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | 0.042990 | -0.0 | 0.018131 | 0.011598 | 0.004224 | -0.0 | -0.0 | -0.0 | -0.014306 | 0.015581 | 0.071734 | 0.015658 | 0.037206 | 0.049469 | -0.0 | -0.0 | 0.010438 | -0.0 | -0.0 | -0.041402 | -0.079431 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.004309 | -0.015553 | -0.0 | -0.0 | -0.0 | -0.126203 | -0.0 | -0.119779 | -0.121182 | -0.0 | -0.134107 | -0.175189 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.049415 | -0.0 | 0.139727 | -0.0 | 0.029609 | -0.001452 | -0.026230 | -0.0 | 0.011992 | 0.036314 | -0.000074 | 0.032367 | 0.039500 | 0.036314 | -0.000836 | 0.005772 | 0.005772 | -0.0 | 0.006450 | 0.005076 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | 0.000724 | -0.010546 | 0.118093 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | 0.063594 | -0.012051 | 0.019572 | -0.067867 | -0.047615 | 0.006432 | -0.035581 | -0.008530 | 0.003469 | 0.020018 | -0.007049 | -0.021222 | -0.035259 | 0.000708 | -0.022600 | -0.101570 | -0.118776 | -0.055221 | -0.031659 | -0.089724 | -0.025832 | -0.161236 | -0.117220 | -0.142956 | -0.046711 | 0.024060 | -0.023413 | 0.028066 | 0.031141 | -0.072017 | 0.057011 | 0.019739 | -0.083357 | -0.022868 | -0.016470 | -0.107639 | -0.087287 | 0.038265 | -0.013059 | -0.002777 | 0.004962 | -0.041452 | -0.062237 | 0.014589 | 0.063053 | 0.039530 | 0.087161 | 0.015202 | 0.105475 | 0.108501 | 0.034434 | -0.004215 | -0.090672 | 0.158822 | 0.155846 | -0.017967 | -0.004179 | 0.179098 | -0.030049 | -0.060296 | 0.004310 | -0.082852 | 0.051327 | 0.138182 | -0.041152 | -0.020716 | 0.104402 | -0.0 | 0.063293 | -0.011568 | 0.020512 | -0.068028 | -0.046495 | 0.009387 | -0.036065 | -0.008305 | 0.003409 | 0.020995 | -0.006684 | -0.019518 | -0.035183 | 0.001436 | -0.021723 | -0.097348 | -0.120100 | -0.055351 | -0.032459 | -0.089125 | -0.025949 | -0.163205 | -0.117852 | -0.144925 | -0.047722 | 0.024957 | -0.023598 | 0.028793 | 0.031323 | -0.072444 | 0.056156 | 0.018122 | -0.078593 | -0.021375 | -0.016501 | -0.107952 | -0.086560 | 0.037996 | -0.012322 | -0.002841 | 0.003315 | -0.044061 | -0.064218 | 0.007895 | 0.063623 | 0.040998 | 0.086370 | 0.014759 | 0.107342 | 0.108748 | 0.035220 | -0.001217 | -0.101133 | 0.159759 | 0.156709 | -0.018484 | -0.006757 | 0.178954 | -0.029026 | -0.060478 | 0.004285 | -0.083640 | 0.051736 | 0.138007 | -0.036688 | -0.026776 | 0.127430 | -0.0 | -0.0 | 0.050636 | 0.075515 | 0.004112 | -0.023481 | 0.008620 | 0.034013 | -0.013003 | -0.071649 | -0.114943 | 0.011646 | -0.051948 | -0.013036 | 0.039021 | -0.0 | -0.042561 | -0.011836 | -0.019821 | -0.008228 | -0.0 | -0.0 | -0.0 | 0.063428 | 0.151416 | -0.0 | -0.0 | 0.025468 | -0.008786 | 0.026361 | 0.016305 | -0.042277 | 0.002868 | 0.031481 | 0.013469 | -0.000843 | -0.025336 | -0.025336 | -0.025044 | -0.025618 | 0.016091 |
| 4 | 0.004716 | -1.430180e-16 | -5.434954e-17 | -8.392303e-17 | 7.696711e-17 | -5.979270e-17 | 0.002666 | 0.000115 | 0.001389 | 0.021637 | -5.969440e-17 | -1.000038e-16 | 0.042541 | 0.006503 | 2.588828e-17 | 0.037708 | -0.023944 | 1.826221e-17 | -5.516496e-17 | -3.158504e-17 | -7.126614e-18 | 2.379529e-17 | -0.001835 | -0.001835 | -0.001079 | -0.002547 | 1.012482e-17 | -3.062674e-17 | -3.047211e-17 | 3.380177e-17 | -4.464840e-18 | 6.540691e-18 | -5.002371e-17 | 5.190371e-18 | -1.877184e-17 | -9.274039e-19 | -5.063075e-19 | -1.451145e-17 | -2.960811e-18 | -2.194715e-17 | 1.746023e-17 | 8.405538e-18 | -1.148994e-18 | 4.534027e-18 | -8.346499e-18 | 2.591063e-19 | -1.549356e-18 | -0.013421 | -0.003895 | 0.014448 | -0.0 | -0.037604 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.024591 | -0.0 | -0.020505 | -0.012579 | -0.003640 | -0.0 | -0.0 | -0.0 | 0.052477 | -0.000804 | -0.004223 | 0.019977 | 0.030445 | -0.026840 | -0.0 | -0.0 | -0.007608 | -0.0 | -0.0 | 0.001389 | 0.021917 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.009209 | 0.006807 | -0.0 | -0.0 | -0.0 | -0.180702 | -0.0 | -0.183090 | -0.206424 | -0.0 | -0.175900 | 0.111883 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.015232 | -0.0 | -0.000609 | -0.0 | 0.001969 | -0.014887 | 0.028886 | -0.0 | 0.015681 | 0.028646 | -0.007321 | 0.030711 | 0.027023 | 0.028646 | 0.004105 | -0.017183 | -0.017183 | -0.0 | -0.014958 | -0.019338 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.006184 | 0.011599 | 0.003413 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | 0.026977 | 0.073637 | 0.067760 | 0.058773 | -0.067721 | -0.007285 | -0.021224 | -0.004469 | 0.062308 | 0.087876 | -0.016289 | 0.036441 | -0.006380 | -0.031238 | 0.024200 | 0.015971 | 0.008675 | 0.084179 | -0.013023 | 0.081733 | 0.048306 | -0.214261 | 0.222455 | -0.101440 | 0.088093 | -0.003817 | -0.008353 | -0.038698 | 0.038793 | -0.060747 | -0.099683 | -0.015027 | -0.017912 | 0.017568 | 0.028049 | -0.046073 | -0.012666 | 0.050067 | -0.173576 | -0.001848 | -0.054285 | -0.208127 | 0.014619 | 0.007151 | -0.148172 | -0.143806 | -0.076754 | -0.110897 | -0.137987 | 0.027116 | 0.039536 | -0.029703 | -0.004117 | 0.040295 | 0.027499 | 0.092501 | -0.061497 | 0.014700 | 0.000639 | 0.025643 | 0.022872 | 0.030146 | 0.022074 | -0.054885 | 0.053665 | 0.080355 | 0.003059 | -0.0 | 0.026535 | 0.074670 | 0.069085 | 0.059135 | -0.069760 | -0.008299 | -0.019740 | -0.004229 | 0.062245 | 0.087860 | -0.016967 | 0.040069 | -0.004271 | -0.027570 | 0.024083 | 0.016790 | 0.011181 | 0.087332 | -0.010615 | 0.083171 | 0.050282 | -0.212491 | 0.221414 | -0.099466 | 0.091350 | 0.000063 | -0.008181 | -0.037953 | 0.038919 | -0.061458 | -0.098551 | -0.013801 | -0.016816 | 0.020193 | 0.028182 | -0.044125 | -0.008362 | 0.049741 | -0.171934 | -0.001856 | -0.053998 | -0.206596 | 0.015718 | 0.011897 | -0.147152 | -0.143143 | -0.075278 | -0.108774 | -0.139947 | 0.029311 | 0.040851 | -0.028016 | -0.006270 | 0.041770 | 0.029302 | 0.094845 | -0.056585 | 0.015553 | 0.000138 | 0.025403 | 0.022012 | 0.029312 | 0.014971 | -0.050310 | 0.047915 | 0.067537 | -0.057710 | -0.0 | -0.0 | 0.029579 | 0.026103 | 0.031212 | 0.050535 | 0.018911 | 0.056165 | -0.003675 | 0.041201 | 0.084287 | -0.005597 | 0.021186 | -0.056053 | 0.037368 | -0.0 | 0.039777 | 0.016025 | -0.039613 | -0.041406 | -0.0 | -0.0 | -0.0 | -0.165740 | -0.129932 | -0.0 | -0.0 | -0.032208 | 0.009972 | -0.018506 | -0.006533 | 0.000783 | -0.028449 | 0.007487 | -0.014164 | 0.004104 | 0.017963 | 0.017963 | 0.017518 | 0.018443 | -0.019350 |
# Top 10 loading scores of PC1
loading_scores = pd.Series(data=pca_statetest.components_[0], index = test_newstate_df.columns)
sorted_loading_scores = loading_scores.abs().sort_values(ascending=False)
top_10 = sorted_loading_scores[0:10].index.values
print(loading_scores[top_10])
OWNER_CY 0.087039 VALBASE_CY 0.087039 HINC35_CY 0.086732 FEM75_CY 0.086474 MALE80_CY 0.086432 OLDRGENSCY 0.086322 ASSCDEG_CY 0.086244 NONHISP_CY 0.086238 POP80_CY 0.086152 WIDOWED_CY 0.085940 dtype: float64
These loading score values are very similar which means a lot of the variables played a role in creating the principal component. The loading score details can be seen in dataframe of components above. The composition also tells us that our component is not depend on a few variables but many variables were used.
sns.set(rc={'figure.figsize':(32,8.27)})
sns.heatmap(pca_statetest.components_[0:4], xticklabels=test_newstate_df.columns, annot=True)
<matplotlib.axes._subplots.AxesSubplot at 0x2025bc56400>
The idea of clustering is to find out if observations in the data naturally group together in some predictable way. In our case, we want to know if there is natural grouping that distinguishes some states from others. Standardization - most cluster analysis algorithms depend on the concept of measuring the distance between the different observations we're trying to cluster. If one of the variables is measured on a much larger scale than the other variables, then whatever measure we use will be overly influenced by the variable that has larger scale. So, we standardize our data to bring all variables to the same scale.
test_newstate_df.head()
# test_newstate_df[(test_newstate_df[[0, 1]] < 0).all(1)]
# test_newstate_df[(test_newstate_df.iloc[:,:] < 0).all(1)]
| Provider_Count | AAGEBASECY | AGGDI_CY | AGGHINC_CY | AGGINC_CY | AGGNW_CY | AIFBASE_CY | AIMBASE_CY | AMERIND_CY | AREA | ASIAN_CY | ASNFBASECY | ASNMBASECY | ASSCDEG_CY | AVGDI_CY | AVGFMSZ_CY | AVGHHSZ_CY | AVGHINC_CY | AVGNW_CY | AVGVAL_CY | BABYBOOMCY | BACHDEG_CY | BAGEBASECY | BLACK_CY | BLKFBASECY | BLKMBASECY | CIVLBFR_CY | EDUCBASECY | EMP_CY | FAMHH_CY | FAMPOP_CY | FEM0_CY | FEM15_CY | FEM18UP_CY | FEM20_CY | FEM21UP_CY | FEM25_CY | FEM30_CY | FEM35_CY | FEM40_CY | FEM45_CY | FEM50_CY | FEM55_CY | FEM5_CY | FEM60_CY | FEM65_CY | FEM70_CY | FEM75_CY | FEM80_CY | FEM85_CY | FEMALES_CY | GED_CY | GENALPHACY | GENBASE_CY | GENX_CY | GENZ_CY | GQPOP_CY | GRADDEG_CY | HAGEBASECY | HHPOP_CY | HINC0_CY | HINC150_CY | HINC15_CY | HINC25_CY | HINC35_CY | HINC50_CY | HINC75_CY | HINCBASECY | HISPAI_CY | HISPASN_CY | HISPBLK_CY | HISPMLT_CY | HISPOTH_CY | HISPPI_CY | HISPPOP_CY | HISPWHT_CY | HSGRAD_CY | HSPFBASECY | HSPMBASECY | IAGEBASECY | LANDAREA | MAL18UP_CY | MAL21UP_CY | MALE0_CY | MALE15_CY | MALE20_CY | MALE25_CY | MALE30_CY | MALE35_CY | MALE40_CY | MALE45_CY | MALE50_CY | MALE55_CY | MALE5_CY | MALE60_CY | MALE65_CY | MALE70_CY | MALE75_CY | MALE80_CY | MALE85_CY | MALES_CY | MARBASE_CY | MARRIED_CY | MEDAGE_CY | MEDDI_CY | MEDFAGE_CY | MEDHHR_CY | MEDHINC_CY | MEDMAGE_CY | MEDNW_CY | MEDVAL_CY | MILLENN_CY | MINORITYCY | MLTFBASECY | MLTMBASECY | NEVMARR_CY | NHSPAI_CY | NHSPASN_CY | NHSPBLK_CY | NHSPMLT_CY | NHSPOTH_CY | NHSPPI_CY | NHSPWHT_CY | NOHS_CY | NONHISP_CY | OAGEBASECY | OLDRGENSCY | OTHFBASECY | OTHMBASECY | OTHRACE_CY | OWNER_CY | PACIFIC_CY | PAGEBASECY | PCI_CY | PIFBASE_CY | PIMBASE_CY | POP0_CY | POP15_CY | POP18UP_CY | POP20_CY | POP21UP_CY | POP25_CY | POP30_CY | POP35_CY | POP40_CY | POP45_CY | POP50_CY | POP55_CY | POP5_CY | POP60_CY | POP65_CY | POP70_CY | POP75_CY | POP80_CY | POP85_CY | POPDENS_CY | RACE2UP_CY | RACEBASECY | RENTER_CY | SMCOLL_CY | SOMEHS_CY | TADULT01 | TADULT02 | TADULT03 | TADULT04 | TADULT05 | TADULT06 | TADULT07 | TADULT08 | TADULT09 | TADULT11 | TADULT12 | TADULT13 | TADULT14 | TADULT15 | TADULT16 | TADULT17 | TADULT18 | TADULT19 | TADULT20 | TADULT21 | TADULT22 | TADULT23 | TADULT24 | TADULT25 | TADULT26 | TADULT27 | TADULT28 | TADULT29 | TADULT30 | TADULT31 | TADULT32 | TADULT33 | TADULT34 | TADULT35 | TADULT36 | TADULT37 | TADULT38 | TADULT39 | TADULT40 | TADULT41 | TADULT42 | TADULT43 | TADULT44 | TADULT45 | TADULT46 | TADULT47 | TADULT48 | TADULT49 | TADULT50 | TADULT51 | TADULT52 | TADULT53 | TADULT54 | TADULT55 | TADULT56 | TADULT57 | TADULT58 | TADULT59 | TADULT60 | TADULT61 | TADULT62 | TADULT63 | TADULT64 | TADULT65 | TADULT66 | TADULT67 | TADULT68 | TADULTBASE | THH01 | THH02 | THH03 | THH04 | THH05 | THH06 | THH07 | THH08 | THH09 | THH11 | THH12 | THH13 | THH14 | THH15 | THH16 | THH17 | THH18 | THH19 | THH20 | THH21 | THH22 | THH23 | THH24 | THH25 | THH26 | THH27 | THH28 | THH29 | THH30 | THH31 | THH32 | THH33 | THH34 | THH35 | THH36 | THH37 | THH38 | THH39 | THH40 | THH41 | THH42 | THH43 | THH44 | THH45 | THH46 | THH47 | THH48 | THH49 | THH50 | THH51 | THH52 | THH53 | THH54 | THH55 | THH56 | THH57 | THH58 | THH59 | THH60 | THH61 | THH62 | THH63 | THH64 | THH65 | THH66 | THH67 | THH68 | THHBASE | THHGRPL1 | THHGRPL11 | THHGRPL12 | THHGRPL13 | THHGRPL14 | THHGRPL2 | THHGRPL3 | THHGRPL4 | THHGRPL5 | THHGRPL6 | THHGRPL7 | THHGRPL8 | THHGRPL9 | THHGRPU1 | THHGRPU2 | THHGRPU3 | THHGRPU4 | THHGRPU5 | THHGRPU6 | TOTHH_CY | TOTHU_CY | TOTPOP_CY | TSEGNUM | UNEMPRT_CY | UNEMP_CY | VACANT_CY | VAL0_CY | VAL150K_CY | VAL1M_CY | VAL1PT5MCY | VAL250K_CY | VAL2M_CY | VAL50K_CY | VAL750K_CY | VALBASE_CY | WAGEBASECY | WHITE_CY | WHTFBASECY | WHTMBASECY | WIDOWED_CY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7353.0 | 5808539 | 984770070408 | 1341852609490 | 1363545956974 | 10953288740347 | 186300 | 188412 | 374712 | 158042.920912 | 5808539 | 3057816 | 2750723 | 2056384 | 73844 | 3.50 | 2.92 | 100620 | 821339 | 637422 | 8055682 | 5552919 | 2345048 | 2345048 | 1186331 | 1158717 | 19595308 | 26629106 | 18678853 | 9166028 | 32063051 | 1246195 | 1267704 | 15535129 | 1399534 | 14709444 | 1533458 | 1442776 | 1332317 | 1220853 | 1252603 | 1262625 | 1290328 | 1247274 | 1173091 | 1008278 | 746364 | 527232 | 362402 | 447803 | 20024285 | 621048 | 1008060 | 39806791 | 8011720 | 9289070 | 813593 | 3342632 | 15757962 | 38993198 | 1280615 | 1084396 | 1102410 | 1036596 | 1486913 | 2149782 | 1630222 | 13335897 | 214901 | 94837 | 149373 | 953706 | 7017528 | 17243 | 15757962 | 7310374 | 4804568 | 7811593 | 7946369 | 374712 | 155779.2198 | 15076422 | 14203971 | 1298461 | 1348980 | 1481021 | 1618901 | 1503701 | 1378564 | 1227529 | 1251002 | 1245261 | 1228175 | 1301504 | 1076510 | 891235 | 637267 | 427533 | 272897 | 270401 | 19782506 | 32126345 | 15627854 | 36.2 | 56020 | 37.3 | 51.5 | 69051 | 35.1 | 99297 | 505800 | 10636136 | 25228888 | 1076167 | 1053726 | 11926897 | 159811 | 5713702 | 2195675 | 1176187 | 84542 | 141009 | 14577903 | 2557052 | 24048829 | 7102070 | 2806123 | 3484353 | 3617717 | 7102070 | 7294468 | 158252 | 158252 | 34254 | 79878 | 78374 | 2544656 | 2616684 | 30611551 | 2880555 | 28913415 | 3152359 | 2946477 | 2710881 | 2448382 | 2503605 | 2507886 | 2518503 | 2548778 | 2249601 | 1899513 | 1383631 | 954765 | 635299 | 718204 | 255.5 | 2129893 | 39806791 | 6041636 | 5627462 | 2067041 | 962752 | 447008 | 877339 | 707623 | 1030736 | 998289 | 1614673 | 1324877 | 854053 | 428180 | 1051926 | 640890 | 507501 | 147970 | 237918 | 290669 | 232487 | 111099 | 175840 | 175248 | 27484 | 508845 | 34216 | 94745 | 26920 | 657657 | 2654588 | 898660 | 530885 | 587455 | 321133 | 1177243 | 208733 | 337293 | 394628 | 476335 | 299210 | 59480 | 140493 | 366232 | 156521 | 289040 | 189701 | 202710 | 74475 | 54655 | 10628 | 210357 | 24920 | 79246 | 244837 | 409205 | 166215 | 21790 | 118065 | 25748 | 113377 | 9029 | 1432303 | 1875298 | 534518 | 504305 | 79548 | 103401 | 172871 | 214308 | 121327 | 30611551 | 435771 | 183824 | 367891 | 297569 | 476909 | 507339 | 673663 | 508597 | 428604 | 259020 | 577307 | 263847 | 213335 | 66627 | 109074 | 143993 | 108873 | 52243 | 85167 | 78061 | 12137 | 244469 | 15552 | 49244 | 13249 | 260783 | 911215 | 362738 | 195394 | 210403 | 128040 | 543776 | 111747 | 169541 | 165386 | 226491 | 161769 | 28909 | 76061 | 193740 | 98928 | 146079 | 107181 | 112822 | 31553 | 22139 | 5084 | 95731 | 9515 | 37825 | 135236 | 200618 | 89406 | 11253 | 50951 | 12199 | 59308 | 4548 | 605661 | 641139 | 209939 | 214632 | 38327 | 25756 | 76824 | 50008 | 179 | 13336104 | 1761964 | 474338 | 127006 | 1709698 | 152588 | 2118203 | 1161232 | 543809 | 499350 | 412712 | 2068573 | 1407619 | 734811 | 1827341 | 4374461 | 1267838 | 4807134 | 591397 | 467754 | 13336104 | 14383561 | 39806791 | 28 | 4.7 | 916455 | 1047457 | 190476 | 277000 | 678744 | 195039 | 432916 | 242837 | 160723 | 903689 | 7293065 | 21888277 | 21888277 | 10953440 | 10934837 | 1601462 |
| 1 | 5115.0 | 1428081 | 663797428676 | 846781778790 | 860166554925 | 6544101733836 | 99642 | 101050 | 200692 | 264622.431161 | 1428081 | 736438 | 691643 | 1352089 | 65007 | 3.36 | 2.78 | 82927 | 640876 | 234140 | 5610338 | 3595450 | 3554094 | 3554094 | 1837649 | 1716445 | 13994294 | 18710254 | 13323060 | 7102474 | 23868426 | 1012163 | 958641 | 11007087 | 1023650 | 10392959 | 1088542 | 1028122 | 990111 | 912087 | 908014 | 888197 | 906409 | 1016393 | 815633 | 699159 | 508500 | 349890 | 230272 | 255883 | 14596063 | 796406 | 820030 | 28954616 | 5795902 | 7332348 | 603396 | 1902087 | 11500677 | 28351220 | 1099136 | 601595 | 972153 | 998707 | 1339610 | 1837748 | 1220161 | 10211181 | 108000 | 21467 | 126710 | 446880 | 3229534 | 4993 | 11500677 | 7563093 | 3900291 | 5712914 | 5787763 | 200692 | 261231.7115 | 10619025 | 9978508 | 1049926 | 1010959 | 1068334 | 1146033 | 1057803 | 1000356 | 900336 | 893942 | 864949 | 855845 | 1054854 | 749300 | 623922 | 440199 | 284583 | 169339 | 142828 | 14358553 | 22771838 | 11608584 | 34.8 | 48504 | 35.8 | 49.5 | 57286 | 33.8 | 89454 | 173734 | 7624216 | 16954978 | 459123 | 454026 | 7616564 | 92692 | 1406614 | 3427384 | 466269 | 37515 | 23827 | 11999638 | 1572787 | 17453939 | 3267049 | 1771782 | 1571022 | 1696027 | 3267049 | 6286745 | 28820 | 28820 | 29707 | 14439 | 14381 | 2062089 | 1969600 | 21626112 | 2091984 | 20371467 | 2234575 | 2085925 | 1990467 | 1812423 | 1801956 | 1753146 | 1762254 | 2071247 | 1564933 | 1323081 | 948699 | 634473 | 399611 | 398711 | 110.8 | 913149 | 28954616 | 3924542 | 4037002 | 1554142 | 257841 | 546026 | 1102103 | 504579 | 298473 | 141680 | 25966 | 3941 | 232406 | 435495 | 27305 | 751696 | 718565 | 539687 | 401342 | 297639 | 111651 | 342816 | 282798 | 522498 | 225754 | 193715 | 190129 | 160088 | 240532 | 1662778 | 199271 | 827432 | 1492651 | 7383 | 1369349 | 27222 | 182557 | 574840 | 1656 | 145357 | 286509 | 195161 | 82795 | 95111 | 17082 | 114932 | 97451 | 70012 | 854973 | 411283 | 295295 | 344420 | 112278 | 4120 | 700747 | 410891 | 133118 | 94734 | 161445 | 243184 | 227922 | 183344 | 38365 | 84691 | 621868 | 44070 | 24312 | 72359 | 201481 | 199469 | 60997 | 21626112 | 124524 | 236631 | 510390 | 229203 | 148579 | 78268 | 11804 | 1715 | 123382 | 270425 | 15874 | 334179 | 322524 | 256069 | 194257 | 156003 | 54214 | 166876 | 141026 | 242077 | 106842 | 94754 | 92548 | 81196 | 118859 | 746527 | 76855 | 359005 | 603599 | 3263 | 582375 | 10984 | 106059 | 311328 | 831 | 73537 | 158365 | 91562 | 43855 | 55012 | 10078 | 56954 | 56138 | 36794 | 392512 | 198958 | 135159 | 153282 | 47834 | 2198 | 438819 | 218771 | 73760 | 51764 | 73393 | 116283 | 113907 | 88717 | 18279 | 32041 | 289865 | 21319 | 12423 | 18523 | 95654 | 64280 | 157 | 10211287 | 1249327 | 785312 | 392300 | 373927 | 178457 | 215169 | 344348 | 912772 | 712376 | 736276 | 2371624 | 752666 | 258831 | 670984 | 2543938 | 1519018 | 3382976 | 702334 | 1391880 | 10211287 | 11236543 | 28954616 | 27 | 4.8 | 671234 | 1025256 | 561071 | 985102 | 66551 | 15750 | 527935 | 20493 | 1095680 | 94609 | 6285595 | 19562731 | 19562731 | 9877750 | 9684981 | 1165365 |
| 2 | 5005.0 | 1774868 | 494056143933 | 721252426439 | 733561461654 | 5780147040071 | 57206 | 55978 | 113184 | 48359.759399 | 1774868 | 920657 | 854211 | 1234468 | 65662 | 3.27 | 2.59 | 95857 | 768205 | 451725 | 4524627 | 2844992 | 3202872 | 3202872 | 1720637 | 1482235 | 10297331 | 13980509 | 9750809 | 4711535 | 15422531 | 541010 | 622797 | 8316966 | 692667 | 7896904 | 736087 | 711718 | 665428 | 617325 | 653985 | 691792 | 725143 | 555213 | 671105 | 583941 | 447188 | 321376 | 227864 | 297383 | 10347030 | 556396 | 439969 | 20070143 | 4071725 | 4262278 | 575494 | 2207562 | 3920105 | 19494649 | 910026 | 562411 | 659740 | 628211 | 822195 | 1168671 | 908502 | 7524226 | 58228 | 16280 | 331729 | 299313 | 1562083 | 3773 | 3920105 | 1648699 | 3075758 | 1994498 | 1925607 | 113184 | 47126.3986 | 7603630 | 7176760 | 563187 | 645491 | 694720 | 735814 | 699391 | 649639 | 591431 | 624744 | 651555 | 664052 | 578913 | 595536 | 496269 | 361798 | 245015 | 158479 | 156451 | 9723113 | 16636184 | 7732110 | 39.0 | 51058 | 40.5 | 53.2 | 63751 | 37.6 | 87167 | 322649 | 5071787 | 9104863 | 369440 | 340441 | 6469341 | 54956 | 1758588 | 2871143 | 410568 | 83316 | 6187 | 10965280 | 866788 | 16150038 | 1645399 | 1699757 | 825459 | 819940 | 1645399 | 3928983 | 9960 | 9960 | 36550 | 5029 | 4931 | 1104197 | 1268288 | 15920596 | 1387387 | 15073664 | 1471901 | 1411109 | 1315067 | 1208756 | 1278729 | 1343347 | 1389195 | 1134126 | 1266641 | 1080210 | 808986 | 566391 | 386343 | 453834 | 425.9 | 709881 | 20070143 | 3595416 | 2182364 | 1012181 | 512090 | 105026 | 4623 | 566085 | 222285 | 250800 | 1375859 | 372083 | 36331 | 199329 | 565803 | 94595 | 113012 | 39543 | 373383 | 232188 | 170661 | 324706 | 344500 | 271067 | 329966 | 269307 | 19946 | 113275 | 239738 | 665 | 189396 | 20342 | 1016 | 1005 | 2888 | 696353 | 107184 | 38266 | 1333128 | 73019 | 173289 | 194976 | 25993 | 345965 | 23485 | 24115 | 145463 | 144351 | 204688 | 104636 | 1156 | 28446 | 11960 | 1150953 | 23619 | 17471 | 149676 | 107735 | 83723 | 278978 | 126444 | 50178 | 647696 | 52872 | 63074 | 73315 | 1056586 | 17171 | 106449 | 111054 | 39680 | 15920596 | 227930 | 45156 | 1971 | 245731 | 101807 | 129891 | 578041 | 153655 | 19294 | 114087 | 297684 | 42464 | 49670 | 19702 | 178348 | 115820 | 85493 | 169528 | 177703 | 127239 | 155409 | 132719 | 9562 | 57642 | 120941 | 284 | 63030 | 8326 | 352 | 535 | 1156 | 332041 | 60446 | 20774 | 569263 | 37915 | 95602 | 102941 | 13893 | 191240 | 11264 | 12914 | 83122 | 86221 | 98353 | 50421 | 584 | 14648 | 1916 | 521013 | 13514 | 9730 | 84095 | 58870 | 42581 | 150061 | 70282 | 28208 | 261620 | 17305 | 24745 | 34054 | 494486 | 4732 | 41867 | 10397 | 263 | 7524399 | 622595 | 687222 | 291132 | 832210 | 56996 | 880881 | 853619 | 111836 | 726892 | 603512 | 73683 | 1218982 | 398654 | 2497180 | 1108306 | 940871 | 2104912 | 239022 | 633845 | 7524399 | 8391638 | 20070143 | 7 | 5.3 | 546522 | 867239 | 152062 | 379625 | 179685 | 56246 | 246121 | 99717 | 374729 | 263656 | 3926227 | 12613979 | 12613979 | 6448602 | 6165377 | 977829 |
| 3 | 4133.0 | 585936 | 487005072197 | 613723856820 | 624450149363 | 6548612367402 | 40665 | 42463 | 83128 | 56491.338012 | 585936 | 316402 | 269534 | 1468305 | 59737 | 3.03 | 2.51 | 75281 | 803267 | 282862 | 5125816 | 2763015 | 3423250 | 3423250 | 1775199 | 1648051 | 9939670 | 14984858 | 9428446 | 5273287 | 15994410 | 540535 | 575794 | 8672285 | 647413 | 8288558 | 693040 | 652863 | 622949 | 602161 | 654907 | 696088 | 756439 | 556332 | 740359 | 711079 | 574407 | 426317 | 297361 | 348670 | 10667264 | 630616 | 434241 | 20875686 | 4066365 | 4142104 | 433652 | 1591194 | 5399203 | 20442034 | 951439 | 366646 | 868205 | 873200 | 1196903 | 1521148 | 970483 | 8152474 | 30738 | 13113 | 201778 | 241103 | 848135 | 3477 | 5399203 | 4060859 | 3663685 | 2732325 | 2666878 | 83128 | 53624.7587 | 8132434 | 7737236 | 561572 | 604324 | 667070 | 728403 | 671610 | 629910 | 591323 | 640640 | 666976 | 693978 | 574960 | 651105 | 617390 | 497457 | 358273 | 236773 | 224380 | 10208422 | 17479459 | 8404539 | 42.3 | 43866 | 43.9 | 55.4 | 52098 | 40.6 | 98640 | 212954 | 4819710 | 9694483 | 320999 | 306853 | 5579880 | 52390 | 572823 | 3221472 | 386749 | 50104 | 11742 | 11181203 | 770061 | 15476483 | 898239 | 2287450 | 433750 | 464489 | 898239 | 5193134 | 15219 | 15219 | 29913 | 7485 | 7734 | 1102107 | 1180118 | 16804719 | 1314483 | 16025794 | 1421443 | 1324473 | 1252859 | 1193484 | 1295547 | 1363064 | 1450417 | 1131292 | 1391464 | 1328469 | 1071864 | 784590 | 534134 | 573050 | 389.3 | 627852 | 20875686 | 2959407 | 2990780 | 1107202 | 139564 | 203442 | 252298 | 342663 | 283344 | 129690 | 129485 | 3407 | 78849 | 131796 | 30196 | 479436 | 544452 | 543860 | 408053 | 288055 | 142999 | 417240 | 573770 | 261579 | 85398 | 333616 | 2029 | 115622 | 143470 | 540715 | 227391 | 933973 | 104898 | 70517 | 460679 | 56767 | 240719 | 452235 | 0 | 207742 | 413860 | 143688 | 644510 | 279740 | 779970 | 766863 | 377196 | 100898 | 428649 | 183680 | 8535 | 297341 | 166979 | 48101 | 323008 | 451913 | 233004 | 178840 | 285711 | 217009 | 234897 | 250408 | 90659 | 40210 | 127174 | 79439 | 13767 | 21497 | 103498 | 109161 | 20707 | 16804719 | 64080 | 84617 | 112750 | 150048 | 139641 | 68081 | 53419 | 1152 | 40455 | 79640 | 18059 | 206336 | 237762 | 248795 | 195362 | 149440 | 70108 | 204783 | 289645 | 118846 | 39620 | 162626 | 1011 | 60130 | 70532 | 238120 | 80416 | 393622 | 42245 | 27259 | 185836 | 27665 | 135609 | 234795 | 0 | 103443 | 231898 | 69296 | 347422 | 155361 | 465971 | 402057 | 222582 | 59214 | 190835 | 81384 | 3488 | 138256 | 62390 | 20779 | 187750 | 229070 | 131273 | 90284 | 117945 | 108972 | 124281 | 117164 | 41705 | 15882 | 54240 | 35494 | 7223 | 4930 | 49689 | 35423 | 57 | 8152541 | 551136 | 659156 | 468362 | 154544 | 90042 | 163107 | 111977 | 692893 | 909338 | 452765 | 967498 | 802706 | 1652607 | 229713 | 1692280 | 1574873 | 2924108 | 1011180 | 720330 | 8152541 | 9790195 | 20875686 | 42 | 5.1 | 511224 | 1637654 | 351168 | 819037 | 78889 | 20495 | 557404 | 36949 | 600047 | 111697 | 5192714 | 15242062 | 15242062 | 7772764 | 7469298 | 1199573 |
| 4 | 3210.0 | 462360 | 315983343213 | 411902114464 | 418518490501 | 4125980999869 | 16036 | 16102 | 32138 | 45285.896438 | 462360 | 240313 | 222047 | 749637 | 61749 | 3.02 | 2.46 | 80493 | 806294 | 236846 | 3167219 | 1763503 | 1470953 | 1470953 | 765013 | 705940 | 6619516 | 9125523 | 6217293 | 3282447 | 9921694 | 335276 | 404682 | 5362136 | 433612 | 5076349 | 410643 | 397480 | 389122 | 375532 | 411895 | 447624 | 487329 | 355242 | 461925 | 408531 | 312098 | 231110 | 173218 | 235227 | 6642616 | 345422 | 268314 | 12992598 | 2573675 | 2724068 | 426311 | 1120165 | 987581 | 12566287 | 549498 | 299343 | 505620 | 485138 | 661013 | 933563 | 649666 | 5117219 | 13841 | 4066 | 72801 | 81708 | 393883 | 1223 | 987581 | 420059 | 2817515 | 484730 | 502851 | 32138 | 44742.7032 | 5010352 | 4726214 | 349790 | 417152 | 441081 | 428491 | 405440 | 392100 | 371081 | 405311 | 435283 | 463856 | 369830 | 429073 | 366389 | 265594 | 180573 | 120018 | 120580 | 6349982 | 10822050 | 5289824 | 41.4 | 46696 | 43.0 | 54.9 | 57362 | 39.8 | 126527 | 185452 | 2988934 | 3115252 | 161589 | 152711 | 3726095 | 18297 | 458294 | 1398152 | 232592 | 16338 | 3998 | 9877346 | 286064 | 12005017 | 410221 | 1270388 | 195431 | 214790 | 410221 | 3463374 | 5221 | 5221 | 32212 | 2622 | 2599 | 685066 | 821834 | 10372488 | 874693 | 9802563 | 839134 | 802920 | 781222 | 746613 | 817206 | 882907 | 951185 | 725072 | 890998 | 774920 | 577692 | 411683 | 293236 | 355807 | 290.4 | 314300 | 12992598 | 1653953 | 1457813 | 585404 | 162479 | 224406 | 22707 | 432943 | 210046 | 93639 | 202612 | 1754 | 84748 | 134652 | 24943 | 212966 | 62172 | 134424 | 548302 | 271213 | 517207 | 239410 | 553292 | 575890 | 903871 | 107575 | 38382 | 88283 | 504795 | 7672 | 14931 | 45556 | 6099 | 2557 | 6112 | 108325 | 136072 | 71952 | 14130 | 273695 | 196055 | 165969 | 5495 | 221381 | 17439 | 19227 | 190704 | 111879 | 144975 | 257849 | 36862 | 11142 | 18502 | 63122 | 23301 | 34327 | 146156 | 125056 | 113432 | 220217 | 317061 | 254390 | 17469 | 0 | 2886 | 271578 | 15924 | 0 | 143081 | 121146 | 29470 | 10372488 | 74277 | 98686 | 10729 | 196059 | 101319 | 45957 | 93730 | 645 | 45424 | 82738 | 12942 | 99476 | 28229 | 66028 | 267265 | 135979 | 258794 | 127103 | 278212 | 269579 | 435594 | 52290 | 18313 | 44342 | 263868 | 3585 | 6189 | 20132 | 2629 | 902 | 2884 | 53850 | 73109 | 39839 | 6765 | 137745 | 110110 | 84780 | 2896 | 116222 | 10022 | 10151 | 101801 | 66453 | 70968 | 124551 | 18223 | 5868 | 6096 | 31133 | 14285 | 17713 | 80722 | 66212 | 56799 | 116780 | 175679 | 129971 | 7816 | 0 | 1255 | 123001 | 7629 | 0 | 57457 | 25114 | 82 | 5117327 | 481070 | 210065 | 479229 | 139701 | 82571 | 185756 | 118011 | 193733 | 1067353 | 1083986 | 36321 | 506198 | 307545 | 287794 | 465570 | 1070547 | 1730882 | 522496 | 1039956 | 5117327 | 5703807 | 12992598 | 22 | 6.1 | 402223 | 586480 | 222750 | 599167 | 27840 | 4755 | 326829 | 6624 | 516090 | 45734 | 3463173 | 10297405 | 10297405 | 5261612 | 5035793 | 725053 |
# Check if any values are 0 in the dataframe
# test_newstate_df.isnull().values.any()
test_newstate_df.columns[test_newstate_df.eq(0).any()].tolist()
['TADULT01', 'TADULT02', 'TADULT03', 'TADULT04', 'TADULT05', 'TADULT06', 'TADULT07', 'TADULT08', 'TADULT09', 'TADULT11', 'TADULT12', 'TADULT13', 'TADULT14', 'TADULT15', 'TADULT16', 'TADULT17', 'TADULT18', 'TADULT19', 'TADULT20', 'TADULT21', 'TADULT22', 'TADULT23', 'TADULT24', 'TADULT25', 'TADULT26', 'TADULT27', 'TADULT28', 'TADULT29', 'TADULT30', 'TADULT31', 'TADULT32', 'TADULT33', 'TADULT34', 'TADULT35', 'TADULT36', 'TADULT37', 'TADULT38', 'TADULT39', 'TADULT40', 'TADULT42', 'TADULT43', 'TADULT44', 'TADULT46', 'TADULT47', 'TADULT48', 'TADULT49', 'TADULT50', 'TADULT51', 'TADULT52', 'TADULT53', 'TADULT55', 'TADULT56', 'TADULT57', 'TADULT58', 'TADULT59', 'TADULT60', 'TADULT61', 'TADULT62', 'TADULT63', 'TADULT64', 'TADULT65', 'TADULT67', 'TADULT68', 'THH01', 'THH02', 'THH03', 'THH04', 'THH05', 'THH06', 'THH07', 'THH08', 'THH09', 'THH11', 'THH12', 'THH13', 'THH14', 'THH15', 'THH16', 'THH17', 'THH18', 'THH19', 'THH20', 'THH21', 'THH22', 'THH23', 'THH24', 'THH25', 'THH26', 'THH27', 'THH28', 'THH29', 'THH30', 'THH31', 'THH32', 'THH33', 'THH34', 'THH35', 'THH36', 'THH37', 'THH38', 'THH39', 'THH40', 'THH42', 'THH43', 'THH44', 'THH46', 'THH47', 'THH48', 'THH49', 'THH50', 'THH51', 'THH52', 'THH53', 'THH55', 'THH56', 'THH57', 'THH58', 'THH59', 'THH60', 'THH61', 'THH62', 'THH63', 'THH64', 'THH65', 'THH67', 'THH68', 'THHGRPL13', 'THHGRPL2', 'THHGRPL3', 'THHGRPL4', 'THHGRPL5', 'THHGRPL6', 'THHGRPL7', 'THHGRPU1', 'THHGRPU5', 'THHGRPU6']
test_newstate_df['MEDHINC_CY'] = test_newstate_df['MEDHINC_CY'].astype(float)
type(test_newstate_df['MEDHINC_CY'][0])
numpy.float64
# Transform Data
from sklearn.preprocessing import PowerTransformer
pt_state = PowerTransformer(method='yeo-johnson')
newstate_df_pt = pt_state.fit_transform(test_newstate_df)
C:\Users\mohi9282\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\sklearn\preprocessing\data.py:2863: RuntimeWarning: divide by zero encountered in log loglike = -n_samples / 2 * np.log(x_trans.var())
from sklearn.cluster import KMeans
sns.set(rc={'figure.figsize':(15,8)})
distortions = []
for i in range(1,20):
km = KMeans(n_clusters=i,
init='k-means++',
n_init=10,
max_iter=300,
random_state=0)
km.fit(newstate_df_pt)
distortions.append(km.inertia_)
plt.plot(range(1,20), distortions, marker='o')
plt.xlabel('No. of Clusters')
plt.ylabel('Within Cluster Sum of Squares')
plt.show()
sns.set(rc={'figure.figsize':(15,8)})
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=4)
# Fit the data
kmeans.fit(newstate_df_pt)
# Get the cluster labels
y_pred = kmeans.predict(newstate_df_pt)
# plot the cluster assignments and cluster centers
# newstate_df_std[:,0] includes all values for Provider Count and so on... #103 is MEDAGE_CY
plt.scatter(newstate_df_pt[:, 0], newstate_df_pt[:,103], c=y_pred, cmap="plasma")
plt.scatter(kmeans.cluster_centers_[:, 0],
kmeans.cluster_centers_[:, 103],
marker='^',
c=[0,1,2,3],
s=100,
linewidth=2,
cmap="plasma")
plt.xlabel("Feature 0")
plt.ylabel("Feature 1")
Text(0, 0.5, 'Feature 1')
kmeans.cluster_centers_
array([[ 0.32717284, 0. , 0. , ..., 0.25436349,
0.26167897, 0.14226632],
[-1.24494478, 0. , 0. , ..., -1.25310715,
-1.25743991, -1.25824838],
[ 1.3453939 , 0. , 0. , ..., 1.31024149,
1.30931671, 1.33246836],
[-0.06460048, 0. , 0. , ..., 0.0221897 ,
0.02154708, 0.09289306]])
kmeans.labels_
array([2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 0, 3, 0, 0, 3, 3, 0, 3, 0,
3, 0, 3, 3, 3, 0, 3, 3, 0, 0, 3, 3, 3, 3, 1, 3, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1])
# Create dataframe of Cluster labels and State Names
clusterdf = pd.DataFrame(kmeans.labels_, newstate_obgyn_df['RegionAbbr'])
clusterdf.reset_index(inplace=True)
clusterdf.columns = ['Region','Cluster']
clusterdf.head()
| Region | Cluster | |
|---|---|---|
| 0 | CA | 2 |
| 1 | TX | 2 |
| 2 | NY | 2 |
| 3 | FL | 2 |
| 4 | PA | 2 |
# Count of states in each cluster
clusterdf['Cluster'].value_counts()
3 15 1 14 2 11 0 11 Name: Cluster, dtype: int64
# States in each cluster
clusterdf.groupby('Cluster')['Region'].apply(list)
Cluster 0 [NJ, MA, MD, WA, AZ, CO, CT, MN, OR, NV, UT] 1 [HI, WV, DC, RI, NH, ID, DE, ME, MT, AK, VT, S... 2 [CA, TX, NY, FL, PA, IL, MI, OH, GA, NC, VA] 3 [MO, IN, TN, SC, LA, WI, KY, AL, OK, MS, KS, I... Name: Region, dtype: object
sns.set(rc={'figure.figsize':(32,8.27)})
sns.heatmap(kmeans.cluster_centers_[0:4], xticklabels=test_newstate_df.columns, annot=True)
<matplotlib.axes._subplots.AxesSubplot at 0x1f4c219fd68>
Image source: